An Insight of Meat Industry in Pakistan with Special Reference to Halal Meat: A Comprehensive Review

Muhammad Sohaib,Faraz Jamil 한국축산식품학회 2017 한국축산식품학회지 Vol.37 No.3

Livestock is considered central component in agricultural sector of Pakistan, provides employment to more than 8 million families. Meat and meat products holds pivotal significance in meeting dietary requirements serving as major protein source and provide essential vitamins and minerals. Globally, consumer demand is increasing for healthy, hygienic and safe meat and meat products due to growing population, income level and food choices. As, food choices are mainly influenced by region, religion and economic level. However, religion is one of the major factor to influence the food choices. In this context, halal foods a growing trend, trade estimated to cross USD $ 3 trillion and among this, meat sector contribute about US$ 600 billion. Halal meat and allied products is requirement from Muslims but it is also accepted by non-Muslims due to safe and hygienic nature, nutritious value and superior quality. Pakistan meat industry is vibrant and has seen rigorous developments during last decade as government also showed interest to boost livestock production and processing facilities to meet increasing local and global demand. The industry has potential to grow owing to its natural animal rearing capability, muslim majority country (96% of total population), improvisation of market and consumer preference towards halal meat. Current review debates Pakistan meat industry scenario, production trend, global trade as well as future potential with respect to modernization, processing, distribution and trade. The data presented here is useful for meat producers, processors and people involved in export of Pakistani meat and meat based products.

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

Sohaib, Muhammad,Kim, Jong-Myon WILEY-INTERSCIENCE 2018 SHOCK AND VIBRATION Vol.2018 No.-

<P>Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE-) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.</P>

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

Sohaib, Muhammad,Kim, Jong-Myon WILEY-INTERSCIENCE 2018 Shock and vibration Vol.2018 No.-

Non-alcoholic fatty liver disease and liver secretome

Muhammad Sohaib Khan,이충호,Sang Geon Kim 대한약학회 2022 Archives of Pharmacal Research Vol.45 No.12

Metabolism of carbohydrates and lipids andprotein degradation occurs in the liver and contributes tothe body’s homeostasis by secreting a variety of mediators. Any imbalance in this homeostasis due to excess fatconsumption and the pathologic events accompanying lipotoxicity,autophagy dysregulation, endoplasmic reticulumstress, and insulin resistance may cause disturbances in thesecretion of the proteins from the liver and their physiologicmodifi cations and interactions with others. Since the liversecretome plays a role in the regulation of fuel metabolismand infl ammation not only in the liver per se but also inother organs, the proteins belong to the utmost targets fortreating metabolic and infl ammatory diseases (e.g., COVID-19), depending on the available and feasible approaches tocontrolling their biological eff ects. However, in this era,we still come across new liver-derived proteins but are yetunable to entirely understand the pathologic basis underlyingdisease progression. This review aims to provide anupdated overview of liver secretome biology with explanatorymechanisms with regard to the progression of metabolicand infl ammatory liver diseases.

Distributed Reinforcement Learning for Enhancing Throughput and Fairness of Multichannel Access Systems

Muhammad Sohaib(무하마드 소하이브),Sang-Woon Jeon(전상운) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6

We consider a multichannel random access system in which each user, arriving in the system randomly and remaining activated for a certain duration, accesses a single channel at each time slot to communicate with an access point. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users. We perform extensive simulations on realistic traffic environments and demonstrate that the proposed online learning improves both throughput and fairness compared to the conventional RL approaches and centralized scheduling policies.

Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees

Muhammad Sohaib(무하마드 소하이브),Jongjin Jeong(정종진),Sang-Woon Jeon(전상운) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2

We consider a multichannel random access system in which each user accesses a single channel at each time slot to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users.

Effect of Feeding Saturated Fat on Milk Production and Composition in Crossbred Dairy Cows

Sarwar, Muhammad,Sohaib, Amer,Khan, Muhammad Ajmal,Nisa, Mahr-un Asian Australasian Association of Animal Productio 2003 Animal Bioscience Vol.16 No.2

To see the effect of Beragfat T-300, a by pass fat, on the production and composition of milk, four primiparous crossbred cows in their early lactation were used in a $4{\times}4$ Latin Square Design. Each period was of 30 days including 15 days of adjustment period. The diets were formulated to contain 0, 2.5, 3.5 and 4.5% of Bergafat and were isonitrogenous and isoenergetic. The intake of DM, OM, CP, NDF, ADF, Cellulose and ADL were not affected, however, the EE intake was increased by the supplementation of Bergafat in the diet of cows. The digestibilities of NDF and EE remained unaffected, whereas the digestibilites of DM, OM and CP were reduced. Milk yield remained unaltered, while 4%FCM yield increased as a result of adding Bergafat in the daily ration. Bergafat upto 4.5% of the diet DM can be added in the diet of crossbred cows without any adverse effect on the DM intake and digestibilities of DM and NDF. Furthermore, Bergafat does not cause any butter fat depression in the milk of cows.

Orientation of Youth towards Social Entrepreneurship: An Empirical Study from Pakistan

Syed Sohaib ZUBAIR,Ifrah AYOOB,Kashif ALI,Mukaram Ali KHAN,Muhammad AZAD,Muhammad ZEESHAN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.9

The importance of Entrepreneurship has been widely acknowledged by researchers and practitioners worldwide, however, the idea of Social Entrepreneurship is still considered to be an emerging area. Entrepreneurship is vital not only because of its economic impacts but also because it helps to address issues of poverty and welfare, where it can act as a catalyst for change. The importance of social entrepreneurship is that it serves to turn a profit and find success while helping others throughout the world. They know the power of social enterprise and are eager to serve the societal and economic benefits. This study aims to identify the level of orientation of youth towards social entrepreneurship in Pakistan. The study identifies the role of various factors that affect Social Entrepreneurial Orientation and is conducted following a quantitative research strategy and survey research design where data is collected from 302 individuals. Structural Equation Modelling was used to analyze the data and to test the hypotheses. The main finding of this research is that there is an increasing trend in the orientation towards social entrepreneurship. The exogenous variables namely Perceived Educational Support, Perceived Structural Support, and Perceived Relational Support were found to have positive and significant effects on the endogenous construct of Social Entrepreneurial Orientation.

Bond strength prediction of steel bars in low strength concrete by using ANN

Sohaib Ahmad,Kypros Pilakoutas,Muhammad M. Rafi,Qaiser U. Zaman 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.22 No.2

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi- Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

Optimization of Flame Retardancy & Mechanical Performance of Jute-glass/Epoxy Hybrid Composites

Muhammad Umair,Ayesha Shahbaz,Ahsan Ahmad,Sohaib Arif,Khubab Shaker,Madeha Jabbar,Yasir Nawab 한국섬유공학회 2022 Fibers and polymers Vol.23 No.10

One of the limiting factors of natural fiber composites is their lower flame retardancy (FR) and mechanicalstrength as compared to the glass and other synthetic fiber composites. In this research, FR and mechanical properties of thehybrid jute-glass/epoxy composites were optimized. In the first part, different percentages (1 %, 2 % & 3 %) of zirconiumphosphate (ZrP) particles were mixed in epoxy resin to optimize the flame retardancy and mechanical properties of jute/epoxy composites. 3 % ZrP loaded composite showed improved FR and mechanical (tensile and impact) properties followedby 2 % and 1 % respectively. In the second part, optimized percentage of ZrP particles (3 %) was used to fabricate two (02)jute-glass hybrid epoxy composites, and their mechanical (tensile, flexural and impact) and FR properties were evaluated. Hybrid (H1) samples showed better mechanical and FR properties due to presence of glass layer on the outer side ofcomposite with 3 % ZrP particles loading.