Gender representation in Pakistani print media- a critical analysis

The key objective of this study was to examine the representations of men and women in print media in Pakistan. Gender role stereotyping and sexism in print media is not a low-profile gender issue as printed communication and contents still hold an important place in contemporary digital world. Keeping in view the importance of newspapers as the leading source of credible content/messages, this paper examined gender stereotyping and sexism in print media in Pakistan and attempted to highlight whether print media reproduces or challenges gender stereotypes and sexism? Keeping in view the complexity of sexism in print media, content and discourse analyses were performed on four widely read national newspapers. The findings have been placed within the socio-cultural context of Pakistani society and feminists theories. The study’s findings indicated that print media in Pakistan reinforces gender stereotypes and provide little challenge to gender stereotyped imagery of males and females

Flooding Challenges Pakistan’s Government and the International Community

The flooding and associated devastation that have battered Pakistan since late July 2010 present yet another series of challenges to its government, already contending with violence from extremist groups. The international community would do well to assist the Pakistani government in responding effectively to these challenges. Natural disasters are social as well as environmental events. The poor and marginalized members of society suffer the most. Marginalization is one of the root causes of violence and militancy in Pakistan. As the government of Pakistan responds to the suffering of its people and the damage to the environment and infrastructure, it should seek to provide relief and recovery assistance in ways that contribute to ameliorating marginalization. Disaster managers should ensure that urgent humanitarian demands do not miss the opportunity to achieve relief and recovery in ways that contribute to good governance, sustainable development and stable peace

ck-NN: A Clustered k-Nearest Neighbours Approach for Large-Scale Classification

k-Nearest Neighbor (k-NN) is a non-parametric algorithm widely used for the estimation and classification of data points especially when the dataset is distributed in several classes. It is considered to be a lazy machine learning algorithm as most of the computations are done during the testing phase instead of performing this task during the training of data. Hence it is practically inefficient, infeasible and inapplicable while processing huge datasets i.e. Big Data. On the other hand, clustering techniques (unsupervised learning) greatly affect results if you do normalization or standardization techniques, difficult to determine "k" Value. In this paper, some novel techniques are proposed to be used as pre-state mechanism of state-of-the-art k-NN Classification Algorithm. Our proposed mechanism uses unsupervised clustering algorithm on large dataset before applying k-NN algorithm on different clusters that might running on single machine, multiple machines or different nodes of a cluster in distributed environment. Initially dataset, possibly having multi dimensions, is pass through clustering technique (K-Means) at master node or controller to find the number of clusters equal to the number of nodes in distributed systems or number of cores in system, and then each cluster will be assigned to exactly one node or one core and then applies k-NN locally, each core or node in clusters sends their best result and the selector choose best and nearest possible class from all options. We will be using one of the gold standard distributed framework. We believe that our proposed mechanism could be applied on big data. We also believe that the architecture can also be implemented on multi GPUs or FPGA to take flavor of k-NN on large or huge datasets where traditional k-NN is very slow

Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.Comment: 19,

Lafora Disease Masquerading as Hepatic Dysfunction

Lafora disease is fatal intractable progressive myoclonic epilepsy. It is frequently characterized by epileptic seizures, difficulty walking, muscle spasms, and dementia in late childhood or adolescence. We chronicle here an unusual case of an asymptomatic young male soccer player who presented with elevated liver enzymes. Neurological examination was unremarkable. The diagnostic workup for hepatitis, infectious etiologies, autoimmune disorders, hemochromatosis, Wilson\u27s disease, alpha-1 antitrypsin deficiency, and other related diseases was inconclusive. He subsequently underwent an uneventful percutaneous liver biopsy. Based on the pathognomonic histopathological findings, Lafora disease was considered the likely etiology. The present study is a unique illustration of this rare disorder initially manifesting with abnormal liver enzymes. It underscores the importance of clinical suspicion of Lafora disease in cases with unexplained hepatic dysfunction. Prompt liver biopsy and genetic testing should be performed to antedate the onset of symptoms in these patients

Synthesis, characterization, POM analyses and biological evaluation of n-[(2-methoxy-5- nitrophenyl)]-4-oxo-4-[oxy] butenamide based zinc(II) carboxylate complexes

ABSTRACT. The aim of medicinal chemistry is to links many scientific disciplines and allows the scientists in researching and developing new drugs with enhance and targeted properties. In this article we are exploring the preparation of four new zinc(II) carboxylate complexes based on N-[(2-methoxy-5-nitrophenyl)]-4-oxo-4-[oxy]butenamide which were characterized through FT-IR and EDX studies. The DNA binding ability and binding type of complexes were assessed by spectroscopic (UV-Visible) and viscosity measurements, exhibiting an intercalative pattern of interaction. The synthesized compounds were also assessed to know theoretically about their nature by molecular docking studies resulting also in intercalation mode. Analysis of the complexes for biological applications such as anti-microbial, anti-leishmanial, cytotoxicity and DNA damage activities showed that these complexes carries good anti-microbial, anti-leishmanial activity with no toxicity to human blood thyrocytes and DNA. The bioavailability prediction and drug likeness score has also been evaluated through Insilco studies.                     KEY WORDS: Zn(II) carboxylate complex, DNA binding, Anti-leishmanial activity, Cytotoxicity, Docking study   Bull. Chem. Soc. Ethiop. 2021, 35(2), 365-380. DOI: https://dx.doi.org/10.4314/bcse.v35i2.1

Malnutrition amongst Under-Five Years Children in Swat, Pakistan: Prevalence and Risk Factors

Purpose: To identify malnourished cases and determine their relationship with weaning time and socioeconomic factors in under-5 children in Swat, Pakistan.Methods: This cross-sectional study was conducted at the Pediatric Ward and Outpatients Department (OPD), Saidu Teaching Hospital, Swat, Pakistan using case files from October to December 2011.Results: A total of 186 children were studied to identify malnutrition, out of which 101 (37.7 %) were male and 85 (32.0 %) female. Moreover, 95 (35.7 %) of the mothers were < 30 years of age and 91 (34.0 %) > 30 years. About 33.7 % of the children were weaned before the age of 4 months. The maternal age of 28.6 % of the malnourished children was < 20 years, and about 2l % of the malnourished children were not immunized against eight EPI (Expanded Program on Immunization) target diseases, viz, poliomyelitis, neonatal tetanus, measles, diphtheria, pertussis (whooping cough), hepatitis-B, Hib pneumonia & meningitis, and childhood tuberculosis. Respondents from urban location 98 (36.7 %), while 88 (33.0 %). Based on Gomezfs classification, out of 186 children, 19 (7.1 %) werevictims of malnutrition; mothers of 35.6 % of the children were uneducated and 25.5 % had primary level (5 years) education. The number of siblings per mother was . 5 in the case of 64.8 % of the malnourished children. More than half of the children were at risk of malnutrition.Conclusion: The incidence of malnutrition is about the same for both male and female children. Risk factors for malnutrition in the children include lack of education, teenage pregnancy, lack of immunization, and large family size.Keywords: Malnutrition, Gomezfs classification, Weaning time, Risk  factors, Teenage pregnancy, Swa

EFFECT OF REDUCING SPERM NUMBERS PER INSEMINATION DOSE ON FERTILITY OF CRYOPRESERVED BUFFALO BULL SEMEN

The objective of this study was to evaluate the effect of reducing sperm numbers per insemination dose on fertility of cryopreserved buffalo bull semen. For this purpose, semen was collected at weekly intervals from a Nili-Ravi buffalo bull (Bubalus bubalis) using an artificial vagina in two batches. The ejaculates were split-sampled and diluted at 37°C with tris-citric acid extender having 15x106 or 30x106 motile spermatozoa/0.5 ml. After dilution, the semen was cooled to 4C, equilibrated for 4 hours, packaged in 0.5 ml straws and frozen in programmable cell freezer. Fertility test based on 75-days first service pregnancy rate was determined under field conditions. A total of 500 buffaloes were inseminated with frozen semen and out of these 431 could be followed, 209 for semen straws packaged with 15x106 spermatozoa/straw and 222 for doses filled with 30x106 spermatozoa/straw. The inseminations were performed in two batches and each batch was spread over a period of three months. The fertility rate for sperm concentration of 15x106 spermatozoa/0.5 ml vs. 30x106 spermatozoa/0.5 ml (49.28 vs. 56.75%) was similar (P>0.05). The fertility rates were also similar (P>0.05) in the first and second batch of inseminations performed with 15x106 or 30x106 spermatozoa/0.5 ml straw of cryopreserved semen. In conclusion, reduction of sperm number from 30x106 to 15x106 spermatozoa/0.5 ml dose of insemination did not affect fertility of cryopreserved buffalo bull semen

An improved search ability of particle swarm optimization algorithm for tracking maximum power point under shading conditions

Introduction. Extracting maximum possible power from solar energy is a hot topic of the day as other sources have become costly and lead to pollution. Problem. Dependency on sunlight for power generation makes it unfeasible to extract maximum power. Environmental conditions like shading, partial shading and weak shading are the major aspect due to which the output of photovoltaic systems is greatly affected. Partial shading is the most known issue. Goal. There have been many proposed techniques and algorithms to extract maximum output from solar resources by use of photovoltaic arrays but every technique has had some shortcomings that couldn’t serve the complete purpose. Methodology. Nature inspired algorithms have proven to be good to search global maximum in a partially shaded multipeak curve which includes particle swarm optimization, artificial bee colony algorithm, and flower pollination algorithm. Methods. Particle swarm optimization algorithm is best among these in finding global peaks with less oscillation around maximum power point, less complexity, and easy to implement nature. Particle swarm optimization algorithm has the disadvantage of having a long computational time and converging speed, particularly under strong shading conditions. Originality. In this paper, an improved opposition based particle swarm optimization algorithm is proposed to track the global maximum power point of a solar photovoltaic module. Simulation studies have been carried out in MATLAB/Simulink R2018a. Practical value. Simulation studies have proved that opposition based particle swarm optimization algorithm is more efficient, less complex, more robust, and more flexible and has better convergence speed than particle swarm optimization algorithm, perturb and observe algorithm, hill climbing algorithm, and incremental conductance algorithm.Вступ. Отримання максимально можливої потужності із сонячної енергії є надзвичайно актуальним наразі, оскільки інші джерела енергії стали коштовними та призводять до забруднення. Проблема. Залежність від сонячного світла для вироблення електроенергії унеможливлює отримання максимальної потужності. Умови навколишнього середовища, такі як затінення, часткове затінення і слабке затінення, є основним аспектом, від якого сильно залежить потужність фотоелектричних систем. Часткове затінення – найвідоміша проблема. Мета. Було запропоновано багато методів та алгоритмів для отримання максимальної віддачі від сонячних ресурсів за допомогою фотоелектричних батарей, але кожен метод мав деякі недоліки, які не могли служити досягненню повної мети. Методологія. Алгоритми, натхненні природою, виявилися хорошими для пошуку глобального максимуму на частково затіненій кривій з багатьма піками, включаючи оптимізацію рою частинок, алгоритм штучної бджолиної колонії та алгоритм запилення квітів. Методи. Алгоритм оптимізації рою частинок найкраще підходить для пошуку глобальних піків з меншими коливаннями навколо точки максимальної потужності, меншою складністю та простотою реалізації. Алгоритм оптимізації рою частинок має недолік, що полягає у тривалому часі обчислень та швидкості збіжності, особливо в умовах сильного затінення. Оригінальність. У цій статті пропонується покращений алгоритм оптимізації рою частинок на основі протилежності для відстеження глобальної точки максимальної потужності сонячного фотоелектричного модуля. Розрахункові моделювання проводились у MATLAB/Simulink R2018a. Практична цінність. Дослідження за допомогою моделювання довели, що алгоритм оптимізації рою частинок на основі протилежності є більш ефективним, менш складним, надійнішим і гнучкішим і має кращу швидкість збіжності, ніж алгоритм оптимізації рою частинок, алгоритм збурення та спостереження, алгоритм сходження на пагорб та алгоритм інкрементальної провідності

3D Object classification using a volumetric deep neural network: An efficient Octree Guided Auxiliary Learning approach

We consider the recent challenges of 3D shape analysis based on a volumetric CNN that requires a huge computational power. This high-cost approach forces to reduce the volume resolutions when applying 3D CNN on volumetric data. In this context, we propose a multiorientation volumetric deep neural network (MV-DNN) for 3D object classification with octree generating low-cost volumetric features. In comparison to conventional octree representations, we propose to limit the octree partition to a certain depth to reserve all leaf octants with sparsity features. This allows for improved learning of complex 3D features and increased prediction of object labels at both low and high resolutions. Our auxiliary learning approach predicts object classes based on the subvolume parts of a 3D object that improve the classification accuracy compared to other existing 3D volumetric CNN methods. In addition, the influence of views and depths of the 3D model on the classification performance is investigated through extensive experiments applied to the ModelNet40 database. Our deep learning framework runs significantly faster and consumes less memory than full voxel representations and demonstrate the effectiveness of our octree-based auxiliary learning approach for exploring high resolution 3D models. Experimental results reveal the superiority of our MV-DNN that achieves better classification accuracy compared to state-of-art methods on two public databases