An economic and environmental analysis of greenhouse tomato production in Norway using a model-based technique

The growing global population levels and the resulting increasing demands for food has put a lot of pressure on the food production systems and made the agricultural sector highly energy-intensive. The intensification in global food production has led to the need to adapt production systems according to the local climatic conditions, making food production possible in areas where it was di cult before and also making the production process environmentally sustainable. One way to adapt food production systems is through protected cultivation techniques, such as greenhouses, that enable controlled indoor climate, crop protection from extreme climate conditions, pests and diseases and the possibility to extend production seasons for certain crops. Yet these techniques a ect the investments, economic performance, used resources and have certain environmental consequences. Norway, for instance, is one such region in which one of the biggest challenges associated with protected cultivation systems is the issue of low availability of natural light and heat, especially during the cold winter months. Production in such regions requires high levels of energy, yet some of these regions also have significant availability of renewable energy resources. The challenge of low light and heat can be overcome by bringing about changes in the production techniques, including greenhouse design elements, production seasons and energy sources. However, this also in turn raises the issue of environmental impact of greenhouse vegetable production in high latitude regions and especially from the use of renewable energy that is present in significant amounts in many regions with considerable greenhouse vegetable production. While there exist several studies on the di erent aspects of greenhouse vegetable production in various regions, and their resulting environmental effects, works related to the use of renewable energy sources, especially in high latitude regions such as Norway are limited. Moreover, studies regarding the environmental impact of greenhouse production of vegetables often show that there is a trade-off between the economic performance and the environmental impact. Local climate and light variability call for regionally adapted greenhouse production techniques. Moreover, the impact of a certain greenhouse design on the economic performance may not always be correlated to the environmental impact. Thus, there is a need to evaluate the impact of various production strategies on the economic potential, resource use and the environment in instances where the traditional fossil fuel is supplemented and/or replaced by energy from renewable resources. In the present work, an attempt has been made to provide a broad picture of greenhouse tomato production at high latitude regions as a result of adapting production strategies in line with the local climates in Norway, with a particular emphasis on renewable energy sources in order to evaluate the environmental impact of locally produced tomatoes that are also economically profitable. The study has been divided into three stages. In the first part, an economic evaluation of seasonal (mid-March to mid-October) greenhouse tomato production in southestern, southwestern, central and northern Norway was performed. In the second part, an economic evaluation and energy use of extended season (from 20th January to 20th November) and year-round production of greenhouse tomatoes in the selected locations in Norway was performed. Sets of plausible design elements, greenhouse climate management, different artificial lighting strategies were assessed to evaluate the impact of the greenhouse design on the Net Financial Return (NFR), energy use and CO2 emissions of the production process. In the third part, a life cycle impact assessment was conducted for a selected number of designs from the first two stages that yielded high NFR or was associated with low energy use in order to assess whether the designs that performed well economically are also environmentally sustainable. The study found clear region-dependent differences in the NFR, its underlying elements, energy use and the resulting environmental impact of different greenhouse designs with differing energy-saving and internal climate control equipment. Our results show that economic profitability can be combined with a low environmental impact under certain regions and production techniques. It was found that Kise (southeastern) was the most favorable location for seasonal greenhouse tomato production in Norway, while Orre (southwestern) was the most favorable location in terms of the economic performance and environmental impact during the extended and year-round production seasons. Moreover, our results show that night energy screens, electric heat pumps and light sources had the most impacts of the elements that were investigated on the NFR and the resulting environmental impact across the three production seasons and need to be considered while constructing greenhouses for tomato production in regions having similar climate as that of Norway. The results of this study provide interesting insights on works related to the greenhouse vegetable production and energy resources in high latitude regions with considerable supplies of renewable energy. The findings can enable local producers across Norway to design greenhouses keeping in mind the local climate, the economic profitability and the environmental sustainability and can help policymakers in devising policies that encourage local growers to adapt production strategies aimed at increasing local production that is both economically profitable and environmentally sustainable

Bio-bibliometric Study of Dr. Khalid Mahmood’s Contributions to LIS Field in Pakistan

This paper presents bio-bibliometric analysis of the contributions of Dr. Khalid Mahmood in the field of Library and Information Science through his publications. The analysis includes geographical and year wise distribution of publications; collaboration for publication; publications by type; language and journal preferences for the publication; and coverage of different subject areas. Results of the study indicate that Dr. Khalid Mahmood is a prolific writer in the field of library and information science. He contributed 115 items including 99 articles, six books, eight conference papers and two papers in newsletters till December 31, 2011. Research work by Dr. Khalid Mahmood is well accepted in developed countries like United Kingdom and United States of America. He used English language to disseminate majority of his research work. He believes in teamwork and about two third of his research work was result of collaboratio

Towards PACE-CAD Systems

Despite phenomenal advancements in the availability of medical image datasets and the development of modern classification algorithms, Computer-Aided Diagnosis (CAD) has had limited practical exposure in the real-world clinical workflow. This is primarily because of the inherently demanding and sensitive nature of medical diagnosis that can have far-reaching and serious repercussions in case of misdiagnosis. In this work, a paradigm called PACE (Pragmatic, Accurate, Confident, & Explainable) is presented as a set of some of must-have features for any CAD. Diagnosis of glaucoma using Retinal Fundus Images (RFIs) is taken as the primary use case for development of various methods that may enrich an ordinary CAD system with PACE. However, depending on specific requirements for different methods, other application areas in ophthalmology and dermatology have also been explored. Pragmatic CAD systems refer to a solution that can perform reliably in day-to-day clinical setup. In this research two, of possibly many, aspects of a pragmatic CAD are addressed. Firstly, observing that the existing medical image datasets are small and not representative of images taken in the real-world, a large RFI dataset for glaucoma detection is curated and published. Secondly, realising that a salient attribute of a reliable and pragmatic CAD is its ability to perform in a range of clinically relevant scenarios, classification of 622 unique cutaneous diseases in one of the largest publicly available datasets of skin lesions is successfully performed. Accuracy is one of the most essential metrics of any CAD system's performance. Domain knowledge relevant to three types of diseases, namely glaucoma, Diabetic Retinopathy (DR), and skin lesions, is industriously utilised in an attempt to improve the accuracy. For glaucoma, a two-stage framework for automatic Optic Disc (OD) localisation and glaucoma detection is developed, which marked new state-of-the-art for glaucoma detection and OD localisation. To identify DR, a model is proposed that combines coarse-grained classifiers with fine-grained classifiers and grades the disease in four stages with respect to severity. Lastly, different methods of modelling and incorporating metadata are also examined and their effect on a model's classification performance is studied. Confidence in diagnosing a disease is equally important as the diagnosis itself. One of the biggest reasons hampering the successful deployment of CAD in the real-world is that medical diagnosis cannot be readily decided based on an algorithm's output. Therefore, a hybrid CNN architecture is proposed with the convolutional feature extractor trained using point estimates and a dense classifier trained using Bayesian estimates. Evaluation on 13 publicly available datasets shows the superiority of this method in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. Explainability of AI-driven algorithms has become a legal requirement after Europe’s General Data Protection Regulations came into effect. This research presents a framework for easy-to-understand textual explanations of skin lesion diagnosis. The framework is called ExAID (Explainable AI for Dermatology) and relies upon two fundamental modules. The first module uses any deep skin lesion classifier and performs detailed analysis on its latent space to map human-understandable disease-related concepts to the latent representation learnt by the deep model. The second module proposes Concept Localisation Maps, which extend Concept Activation Vectors by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. This thesis probes many viable solutions to equip a CAD system with PACE. However, it is noted that some of these methods require specific attributes in datasets and, therefore, not all methods may be applied on a single dataset. Regardless, this work anticipates that consolidating PACE into a CAD system can not only increase the confidence of medical practitioners in such tools but also serve as a stepping stone for the further development of AI-driven technologies in healthcare

Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks

Die zur Erfüllung der zu erwartenden Steigerungen übertragener Datenmengen notwendige größere Heterogenität und steigende Anzahl von Zellen werden in der Zukunft zu einer deutlich höheren Komplexität bei Planung und Optimierung von Funknetzen führen. Zusätzlich erfordern räumliche und zeitliche Änderungen der Lastverteilung eine dynamische Anpassung von Funkabdeckung und -kapazität (Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhängig, was sie zeitaufwändig und teuer macht. Aus diesen Grnden treffen Ansätze zur besseren Automatisierung des Netzwerkmanagements sowohl in der Industrie, als auch der Forschung auf groes Interesse.Selbstorganisationstechniken (SO) haben das Potential, viele der aktuell durch Menschen gesteuerten Abläufe zu automatisieren. Ihnen wird daher eine zentrale Rolle bei der Realisierung eines einfachen und effizienten Netzwerkmanagements zugeschrieben. Die vorliegende Arbeit befasst sich mit selbstorganisierter Optimierung von Abdeckung und Übertragungskapazität in Funkzellennetzwerken. Der Parameter der Wahl hierfür ist die Antennenneigung. Die zahlreichen vorhandenen Ansätze hierfür befassen sich mit dem Einsatz heuristischer Algorithmen in der Netzwerkplanung. Im Gegensatz dazu betrachtet diese Arbeit den verteilten Einsatz entsprechender Optimierungsverfahren in den betreffenden Netzwerkknoten. Durch diesen Ansatz können zentrale Fehlerquellen (Single Point of Failure) und Skalierbarkeitsprobleme in den kommenden heterogenen Netzwerken mit hoher Knotendichte vermieden werden.Diese Arbeit stellt einen "Fuzzy Q-Learning (FQL)"-basierten Ansatz vor, ein einfaches Maschinenlernverfahren mit einer effektiven Abstraktion kontinuierlicher Eingabeparameter. Das CCO-Problem wird als Multi-Agenten-Lernproblem modelliert, in dem jede Zelle versucht, ihre optimale Handlungsstrategie (d.h. die optimale Anpassung der Antennenneigung) zu lernen. Die entstehende Dynamik der Interaktion mehrerer Agenten macht die Fragestellung interessant. Die Arbeit betrachtet verschiedene Aspekte des Problems, wie beispielsweise den Unterschied zwischen egoistischen und kooperativen Lernverfahren, verteiltem und zentralisiertem Lernen, sowie die Auswirkungen einer gleichzeitigen Modifikation der Antennenneigung auf verschiedenen Knoten und deren Effekt auf die Lerneffizienz.Die Leistungsfähigkeit der betrachteten Verfahren wird mittels eine LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmäßig verteilte Zellen, als auch Zellen ungleicher Größe betrachtet. Die entwickelten Ansätze werden mit bekannten Lösungen aus der Literatur verglichen. Die Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf Änderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen sich selbsttätig schnell auf Ausfälle und Inbetriebnahmen benachbarter Systeme ein und passen ihre Antennenneigung geeignet an um die Gesamtleistung des Netzes zu verbessern. Die vorgestellten Lernverfahren erreichen eine bis zu 30 Prozent verbesserte Leistung als bereits bekannte Ansätze. Die Verbesserungen steigen mit der Netzwerkgröße.The challenging task of cellular network planning and optimization will become more and more complex because of the expected heterogeneity and enormous number of cells required to meet the traffic demands of coming years. Moreover, the spatio-temporal variations in the traffic patterns of cellular networks require their coverage and capacity to be adapted dynamically. The current network planning and optimization procedures are highly manual, which makes them very time consuming and resource inefficient. For these reasons, there is a strong interest in industry and academics alike to enhance the degree of automation in network management. Especially, the idea of Self-Organization (SO) is seen as the key to simplified and efficient cellular network management by automating most of the current manual procedures. In this thesis, we study the self-organized coverage and capacity optimization of cellular mobile networks using antenna tilt adaptations. Although, this problem is widely studied in literature but most of the present work focuses on heuristic algorithms for network planning tool automation. In our study we want to minimize this reliance on these centralized tools and empower the network elements for their own optimization. This way we can avoid the single point of failure and scalability issues in the emerging heterogeneous and densely deployed networks.In this thesis, we focus on Fuzzy Q-Learning (FQL), a machine learning technique that provides a simple learning mechanism and an effective abstraction level for continuous domain variables. We model the coverage-capacity optimization as a multi-agent learning problem where each cell is trying to learn its optimal action policy i.e. the antenna tilt adjustments. The network dynamics and the behavior of multiple learning agents makes it a highly interesting problem. We look into different aspects of this problem like the effect of selfish learning vs. cooperative learning, distributed vs. centralized learning as well as the effect of simultaneous parallel antenna tilt adaptations by multiple agents and its effect on the learning efficiency.We evaluate the performance of the proposed learning schemes using a system level LTE simulator. We test our schemes in regular hexagonal cell deployment as well as in irregular cell deployment. We also compare our results to a relevant learning scheme from literature. The results show that the proposed learning schemes can effectively respond to the network and environmental dynamics in an autonomous way. The cells can quickly respond to the cell outages and deployments and can re-adjust their antenna tilts to improve the overall network performance. Additionally the proposed learning schemes can achieve up to 30 percent better performance than the available scheme from literature and these gains increases with the increasing network size

Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs

Owing to their remarkable learning (and relearning) capabilities, deep neural networks (DNNs) find use in numerous real-world applications. However, the learning of these data-driven machine learning models is generally as good as the data available to them for training. Hence, training datasets with long-tail distribution pose a challenge for DNNs, since the DNNs trained on them may provide a varying degree of classification performance across different output classes. While the overall bias of such networks is already highlighted in existing works, this work identifies the node bias that leads to a varying sensitivity of the nodes for different output classes. To the best of our knowledge, this is the first work highlighting this unique challenge in DNNs, discussing its probable causes, and providing open challenges for this new research direction. We support our reasoning using an empirical case study of the networks trained on a real-world dataset.Comment: To appear at the 16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023), Dublin, Irelan

Motivation Techniques Used By Heads Of Higher Educational Institutions In Pakistan

The study was aimed to investigate the Motivation techniques used by Heads Higher Educational Institutions in Pakistan. The population of this study comprised all the heads of degree colleges of Pakistan. For choosing the sample from this population, random sampling type was used. The sample constituted of 200 heads of degree colleges and colleges and 1000 teachers of degree colleges. A questionnaire was used as research instruments for collection of data. Collected data were tabulated, analyzed and interpreted in the light of objectives of the study by applying statistical tools of research such as chi-square as a contingency test and percentage were used. Chi-square as contingency test was used to compare the frequencies of principals/teachers. In the light of the conclusions it is recommended that a special training course may be arranged for educational managers, administrators and supervisors for achieving competency in motivation techniques

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Design Of Intelligent Stick Based On Microcontroller With GPS Using Speech IC

For visually impaired persons the endeavors and researchers have spent the decades to develop an intelligent and smart stick to protect and alert them from obstacles. This paper proposes a new thought developing an intelligent stick equipped with GPS navigation system, which detect the obstacles in path and gives information about their location using GPS coordinates. The combination of ultrasonic sensors and GPS will detect the obstacles and determine the position and will gives information about location through Bluetooth.DOI:http://dx.doi.org/10.11591/ijece.v2i6.162