Impact of Financing on Sales Growth

Purpose: The purpose of the study is to examine the impact of credit supply and long term financing on sales growth in manufacturing sector.Design/methodology/approach: It is causal study; in which secondary data from audited annual reports of manufacturing firms are included. Findings: It is proved that long term financing has significant negative effect on sale growth. While, trade credit and lagged sales growth are found to increase sales.Practical implications: It has practical implication from manager’s perspectives. It is beneficial for managers to increase their firm’s growth efficiently by managing the supply of trade credit. It will also enhance their skill to manage long term loans so that firm’s sales can’t effected badly. Originality Value: It is the unique study as it provide evidence that how sales growth of the manufacturing sector can be effected by granting trade credit and taking long term financing followed by trade-off theory

Research and development of stock management strategies to optimise growth potential in on-growing of Atlantic cod, Gadus morhua, and Atlantic halibut, Hippoglossus hippoglossus

Aquaculture is an essential developing sector for world food production, however the attainment of sexual maturity during commercial on-growing is a major bottleneck to industry expansion. Sexual maturation brings a commercial loss due to reduced growth performance as well as reduced immune function. Furthermore, serious concerns exist over potential genetic interaction with native stocks through broadcast spawning or spawning interaction by escapees. In the north Atlantic region, the Atlantic cod (Gadus morhua) and Atlantic halibut (Hippoglossus hippoglossus) are key aquaculture species in which industry expansion is limited by pre-harvest sexual maturation. However, through a species specific combination of modern technologies and refinement in management practices it is possible that this sexual maturation can be controlled and on-growing potential enhanced. Thus the overall aim of this thesis was to conduct novel research that will improve our understanding of the underlying mechanisms that regulate sexual maturation, whilst also advancing the optimisation of technologies for the management of maturation in cod and halibut. In Atlantic cod, owing to the inconsistent inhibition of maturation in commercial conditions, ever increasing intensities of light and in some cases narrow spectrum technologies are being used to try to combat this problem. Firstly, this PhD project investigated the potential welfare impacts of high intensity artificial lighting which have not been studied to date (Chapter 2). The work specifically investigated the effect of traditional metal halide and novel green cathode lighting on the stress response, innate immunity, retina structure, feeding activity and light perception of Atlantic cod. Results indicated that although acute responses to light were observed, there were no clear significant long term effects of any of the lighting treatments on these parameters. Regarding light perception, interestingly even when subjected to high intensity constant lighting (metal halide mean tank intensity: 16.6 watts m-2), cod still demonstrated a day/night rhythm in melatonin release which suggests perception of the overlying ambient photoperiod. The second trial of this PhD project investigated the efficacy of shading of ambient photoperiod in addition to constant lighting to inhibit maturation of cod outdoors (Chapter 3). This aimed at improving the performance of artificial lighting regimes in the open cage system during commercial on-growing by reducing the relative difference between day/night light intensities. The trial was conducted over a one year period where a low and high shade treatment were tested in outdoor tanks. Shading increased the relative night time illumination to 6.6% and 31.3% of daytime levels respectively, compared to <2% in an unshaded set-up. Both shading treatments were effective at suppressing sexual development in cod as confirmed through measurements of gonadosomatic index, histological analysis of gonadal development, oocyte diameter measurements and sex steroid profiles as well as measurements of growth. In addition to research at the applied level in Atlantic cod, this thesis has also extended to the fundamental level and explored one of the potential mechanisms relaying photoperiod signal to the endogenous regulation of sexual maturation in cod, namely the kisspeptin system (Chapter 4). Partial sequences for the signal peptide Kiss2 and its receptor Kissr4 were isolated and described showing similarity to other teleost species such as the medaka, Oryzias latipes and stickleback, Danio rerio. Novel molecular qPCR assays were designed and developed to measure the expression of both genes in male and female cod over a maturation cycle and compared to cod under constant lighting which remained immature. Interestingly, expression patterns of kiss2 and kissr4 did not reveal any clear association with season or photoperiod treatment. However, pituitary expression of gonadotropins (FSH, follicle stimulating hormone; LH, luteinising hormone) did show a differential expression in relation to treatment from early winter approximately 4-6 months after the photoperiod change. These new results are in contradiction with the hypothesis that the kisspeptin system would be involved in the initiation of gametogenesis, as shown in mammals. However, the FSH/LH data defines a window during which time kisspeptin or another GnRH stimulating mechanism must be active, this compels the need further investigation. In Atlantic halibut farming, all-female production removes the concerns of production losses through sexual maturation. Accordingly, this thesis investigated the potential/feasibility of generating monosex populations by FACS (fluorescence activated cell sorting) semen sexing based on cellular DNA content, as proven in terrestrial agriculture. Results however did not show any clear differences between the DNA of sperm in a range of species tested (Atlantic halibut, cod, sea bass, perch) suggesting that this technique may not be applicable in such species. The project also focussed on the production of a population of sex reversed halibut broodstock (neomales) that will generate, in the long term, a basis for traditional monosex population generation in the UK. Two in feed MDHT (17α-methyldihydrotestosterone) treatments were tested with the aim to reduce the use of hormone. Results were very successful with a hormone treatment of 5ppm MDHT generating a 97% phenotypic male population thus suggesting the presence of sex-reversed halibut which can be used for future monosex production. Overall, this work aimed to develop and/or refine potential remediation techniques for sexual maturation in two key commercially important farmed marine fish species, cod and halibut, as well as further our understanding on the regulation of puberty. The knowledge gained from this work provides a means to optimise the techniques employed in the industry and has the potential to increase production and profitability without compromising farmed animal welfare, thus ultimately promoting the sustainable expansion of the Atlantic cod and halibut aquaculture.EThOS - Electronic Theses Online ServiceScottish Aquaculture Research Forum (027)GBUnited Kingdo

Molecular diagnostics for foodborne pathogen (Salmonella spp.) from poultry

Background: Salmonella species (spp.) are among major food-borne pathogens all over the world. Salmonella typhimurium is the main cause of food poisoning in humans. The fundamental objective of this study is to develop a rapid and reliable method to detect Salmonella (a foodborne pathogen) in raw poultry meat by using molecular approaches. Methods: Total 200 samples of raw poultry meat were collected from different regions of Lahore and analyzed for the presence of Salmonella spp. fimA gene.  Similarly, sent genes were selected for the detection of Salmonella typhimurium and Salmonella enteritidis respectively. PCR technique was optimized for diagnosis of contamination.Results: Out of 200 samples, 2% samples had shown successful amplification of fimA gene representing the presence of serovar Salmonella typhimurium. PCR assay combined with enrichment can enhance the efficiency for detection of Salmonella in poultry. Conclusion: A robust, simple and convenient PCR based method has been developed for the detection of one of the major food-borne pathogen Salmonella typhimurium. 

Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing

False Data Injection Detection for Phasor Measurement Units

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

Reservoir Potential Evaluation of the Middle Paleocene Lockhart Limestone of the Kohat Basin, Pakistan: Petrophysical Analyses

The Lockhart Limestone is evaluated for its reservoir potential by utilizing wireline logs of Shakardara-01 well from Kohat Basin, Pakistan. The analyses showed 28.03% average volume of shale (Vsh), 25.57% average neutron porosity (NPHI), 3.31% average effective porosity (PHIE), 76% average water saturation (Sw), and 24.10% average hydrocarbon saturation (Sh) of the Lockhart Limestone in Shakardara-01 well. Based on variation in petrophysical character, the reservoir units of the Lockhart Limestone are divided into three zones i.e., zone-1, zone-2 and zone-3. Out of these zones, zone-1 and zone-2 possess a poor reservoir potential for hydrocarbons as reflected by very low effective porosity (1.40 and 2.02% respectively) and hydrocarbon saturation (15 and 5.20%), while zone-3 has a moderate reservoir potential due to its moderate effective porosity (6.50%) and hydrocarbon saturation (52%) respectively. Overall, the average effective porosity of 3.31% and hydrocarbon saturation of 24.10% as well as 28.03% volume of shale indicated poor reservoir potential of the Lockhart Limestone. Lithologically, this formation is dominated by limestone and shale interbeds in the Shakardara-01 well. Cross-plots of the petrophysical parameters versus depth showed that the Lockhart Limestone is a poor to tight reservoir in Shakardara-01 well and can hardly produce hydrocarbons under conventional drilling conditions

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring