Driver of Demand in Cargo and Passenger Traffic Between Penta Ports

Maritime transport moves around 6 billion tonnes of freight every year. The freight consists of liquid bulks (45%), dry bulks (23%) and general cargo (32%). Freight traffic and transports chains vary according to region, commodity and the origin and the destination of freight. In the European Union the ports sector handles over 90% of the trade with third countries. The share of intra-EU trade is approximately 30% of the total transportation and the number of passengers is over 200 million every year. The Baltic Sea has more than 50,000 vessels a year pass the Skaw at the northernmost tip of Denmark on their way into or out of the Baltic. Roughly 60% to 70% of these vessels are cargo vessels and 17% to 25% tankers. Ports and maritime transport play a crucial role in global commerce today. Today’s business environment is changing rapidly, and the constant changes create challenges for the transport industry and maritime traffic. Ports have to adapt to continuous changes in economic structures, logistics demands, and people’s travel and leisure patterns. In order to ensure the competitiveness of sea connections, the ports need to fully enhance multilateral cross-border understanding and cooperation. In this report the focus is on liner traffic between five ports in the Central Baltic Region: Stockholm, Tallinn, Helsinki Turku and Naantali. The report defines the drivers of the demand for cargo and passenger traffic and highlights the most important factors. The economic situation and foreign trade of each county are elaborated on with detailed information about the flows of traffic between the five ports. Based on expert interviews, the main characteristics of each port, including strengths and weaknesses, are presented. The report is based on primary and secondary data. Primary data was received through interviews and mail surveys. Secondary data was attained through a literature research, statistics, data given by the PENTA ports and webpages. The report is divided into two main parts: the drivers creating the demand for transport and the results of current cargo and passenger flows between PENTA ports.

Montreal classification of patient operated for Crohn's disease and identification of surgical recurrence predictors

OBJECTIVE: to evaluate Crohn's disease recurrence and its possible predictors in patients undergoing surgical treatment. METHODS: We conducted a retrospective study with Crohn's disease (CD) patients undergoing surgical treatment between January 1992 and January 2012, and regularly monitored at the Bowel Clinic of the Hospital das Clínicas of the UFMG. RESULTS: we evaluated 125 patients, 50.4% female, with a mean age of 46.12 years, the majority (63.2%) diagnosed between 17 and 40 years of age. The ileum was involved in 58.4%, whereas stenotic behavior was observed in 44.8%, and penetrating, in 45.6%. We observed perianal disease in 26.4% of cases. The follow-up average was 152.40 months. Surgical relapse occurred in 29.6%, with a median time of 68 months from the first operation. CONCLUSION: The ileocolic location, penetrating behavior and perianal involvement (L3B3p) were associated with increased risk of surgical recurrence.

Diagnosis, classification and treatment of gestational trophoblastic neoplasia

Gestational trophoblastic neoplasia (GTN) is the term to describe a set of malignant placental diseases, including invasive mole, choriocarcinoma, placental site trophoblastic tumor and epithelioid trophoblastic tumor. Both invasive mole and choriocarcinoma respond well to chemotherapy, and cure rates are greater than 90%. Since the advent of chemotherapy, low-risk GTN has been treated with a single agent, usually methotrexate or actinomycin D. Cases of high-risk GTN, however, should be treated with multiagent chemotherapy, and the regimen usually selected is EMA-CO, which combines etoposide, methotrexate, actinomycin D, cyclophosphamide and vincristine. This study reviews the literature about GTN to discuss current knowledge about its diagnosis and treatment.

Purchasing portfolio analysis and supplier classification

The purpose of the thesis is to classify suppliers and to enhance strategic purchasing in the case company. Supplier classification is conducted to fulfill the requirements of the company quality manual and international quality standards. To gain more benefit, a strategic purchasing tool, Kraljic’s purchasing portfolio and analytical hierarchy process are utilized for the base of supplier classification. Purchasing portfolio is used to give quick and easy visual insight on product group management form the viewpoint of purchasing. From the base on purchasing portfolio alternative purchasing and supplier strategies can be formed that enhance the strategic orientation of purchasing. Thus purchasing portfolio forces the company to orient on proactive and strategic purchasing. As a result a survey method for implementing purchasing portfolio in the company is developed that exploits analytical hierarchy process. Experts from the company appoint the categorization criteria and in addition, participate in the survey to categorize product groups on the portfolio. Alternative purchasing strategies are formed. Suppliers are classified depending on the importance and characteristics of the product groups supplied.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Support vector machines in spectral data classification

Työssä käydään läpi tukivektorikoneiden teoreettista pohjaa sekä tutkitaan eri parametrien vaikutusta spektridatan luokitteluun.

Scenario-based traffic forecasts for the routes between the PENTA ports in 2020

Maritime transport is the foundation for trade in the Baltic Sea area. It represents over 15% of the world’s cargo traffic and it is predicted to increase by over 100% in the future. There are currently over 2,000 ships sailing on the Baltic Sea and both the number and the size of ships have been growing in recent years. Due to the importance of maritime traffic in the Baltic Sea Region, ports have to be ready to face future challenges and adapt to the changing operational environment. The companies within the transportation industry – in this context ports, shipowners and logistics companies – compete continuously and although the number of companies in the business is not particularly substantial because the products offered are very similar, other motives for managing the supply chain arise. The factors creating competitive advantage are often financial and related to cost efficiency, but geographical location, road infrastructure in the hinterland and vessel connections are among the most important factors. The PENTA project focuses on adding openness, transparency and sharing knowledge and information, so that the challenges of the future can be better addressed with regard to cooperation. This report presents three scenario-based traffic forecasts for routes between the PENTA ports in 2020. The chosen methodology is PESTE, in which the focus in on economic factors affecting future traffic flows. The report further analyses the findings and results of the first PENTA WP2 report “Drivers of demand in cargo and passenger traffic between PENTA ports” and utilises the same material, which was obtained through interviews and mail surveys.

Service offering classification for logistics services

This thesis studies the development of service offering model that creates added-value for customers in the field of logistics services. The study focusses on offering classification and structures of model. The purpose of model is to provide value-added solutions for customers and enable superior service experience. The aim of thesis is to define what customers expect from logistics solution provider and what value customers appreciate so greatly that they could invest in value-added services. Value propositions, costs structures of offerings and appropriate pricing methods are studied. First, literature review of creating solution business model and customer value is conducted. Customer value is found out with customer interviews and qualitative empiric data is used. To exploit expertise knowledge of logistics, innovation workshop tool is utilized. Customers and experts are involved in the design process of model. As a result of thesis, three-level value-added service offering model is created based on empiric and theoretical data. Offerings with value propositions are proposed and the level of model reflects the deepness of customer-provider relationship and the amount of added value. Performance efficiency improvements and cost savings create the most added value for customers. Value-based pricing methods, such as performance-based models are suggested to apply. Results indicate the interest of benefitting networks and partnership in field of logistics services. Networks development is proposed to be investigated further.

Use of fluorescent probes to follow membrane traffic in nerve terminals

Optical tracers in conjunction with fluorescence microscopy have become widely used to follow the movement of synaptic vesicles in nerve terminals. The present review discusses the use of these optical methods to understand the regulation of exocytosis and endocytosis of synaptic vesicles. The maintenance of neurotransmission depends on the constant recycling of synaptic vesicles and important insights have been gained by visualization of vesicles with the vital dye FM1-43. A number of questions related to the control of recycling of synaptic vesicles by prolonged stimulation and the role of calcium to control membrane internalization are now being addressed. It is expected that optical monitoring of presynaptic activity coupled to appropriate genetic models will contribute to the understanding of membrane traffic in synaptic terminals.

Designing sustainable industrial ecosystems : the case of a biogas-for-traffic solution

This thesis focuses on the development of sustainable industrial architectures for bioenergy based on the metaphors of industrial symbiosis and industrial ecosystems, which imply exchange of material and energy side-flows of various industries in order to improve sustainability of those industries on a system level. The studies on industrial symbiosis have been criticised for staying at the level of incremental changes by striving for cycling waste and by-flows of the industries ‘as is’ and leaving the underlying industry structures intact. Moreover, there has been articulated the need for interdisciplinary research on industrial ecosystems as well as the need to extend the management and business perspectives on industrial ecology. This thesis addresses this call by applying a business ecosystem and business model perspective on industrial symbiosis in order to produce knowledge on how industrial ecosystems can be developed that are sustainable environmentally and economically. A case of biogas business is explored and described in four research papers and an extended summary that form this thesis. Since the aim of the research was to produce a normative model for developing sustainable industrial ecosystems, the methodology applied in this research can be characterised as constructive and collaborative. A constructive research mode was required in order to expand the historical knowledge on industrial symbiosis development and business ecosystem development into the knowledge of what should be done, which is crucial for sustainability and the social change it requires. A collaborative research mode was employed through participating in a series of projects devoted to the development of a biogas-for-traffic industrial ecosystem. The results of the study showed that the development of material flow interconnections within industrial symbiosis is inseparable from larger business ecosystem restructuring. This included a shift in the logic of the biogas and traffic fuel industry and a subsequent development of a business ecosystem that would entail the principles of industrial symbiosis and localised energy production and consumption. Since a company perspective has been taken in this thesis, the role of an ecosystem integrator appeared as a crucial means to achieve the required industry restructuring. This, in turn, required the development of a modular and boundary-spanning business model that had a strong focus on establishing collaboration among ecosystem stakeholders and development of multiple local industrial ecosystems as part of business growth. As a result, the designed business model of the ecosystem integrator acquired the necessary flexibility in order to adjust to local conditions, which is crucial for establishing industrial symbiosis. This thesis presents a normative model for the development of a business model required for creating sustainable industrial ecosystems, which contributes to approaches at the policy-makers’ level, proposed earlier. Therefore, this study addresses the call for more research on the business level of industrial ecosystem formation and the implications for the business models of the involved actors. Moreover, the thesis increases the understanding of system innovation and innovation in business ecosystems by explicating how business model innovation can be the trigger for achieving more sustainable industry structures, such as those relying on industrial symbiosis.

Noninvasive classification of liver disease in asymptomatic and oligosymptomatic male alcoholics

The predominant type of liver alteration in asymptomatic or oligosymptomatic chronic male alcoholics (N = 169) admitted to a psychiatric hospital for detoxification was classified by two independent methods: liver palpation and multiple quadratic discriminant analysis (QDA), the latter applied to two parameters reported by the patient (duration of alcoholism and daily amount ingested) and to the data obtained from eight biochemical blood determinations (total bilirubin, alkaline phosphatase, glycemia, potassium, aspartate aminotransferase, albumin, globulin, and sodium). All 11 soft and sensitive, and 13 firm and sensitive livers formed fully concordant groups as determined by QDA. Among the 22 soft and not sensitive livers, 95% were concordant by QDA grouping. Concordance rates were low (55%) in the 73 firm and not sensitive livers, and intermediate (76%) in the 50 not palpable livers. Prediction of the liver palpation characteristics by QDA was 95% correct for the firm and not sensitive livers and moderate for the other groups. On a preliminary basis, the variables considered to be most informative by QDA were the two anamnestic data and bilirubin levels, followed by alkaline phosphatase, glycemia and potassium, and then by aspartate aminotransferase and albumin. We conclude that, when biopsies would be too costly or potentially injurious to the patients to varying extents, clinical data could be considered valid to guide patient care, at least in the three groups (soft, not sensitive; soft, sensitive; firm, sensitive livers) in which the two noninvasive procedures were highly concordant in the present study.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Sleep disorders, sleepiness and traffic safety: a public health menace

Sleep disorders are not uncommon and have been widely reported throughout the world. They have a profound impact on industrialized 24-h societies. Consequences of these problems include impaired social and recreational activities, increased human errors, loss of productivity, and elevated risk of accidents. Conditions such as acute and chronic insomnia, sleep loss, excessive sleepiness, shift-work, jet lag, narcolepsy, and sleep apnea warrant public health attention, since residual sleepiness during the day may affect performance of daily activities such as driving a car. Benzodiazepine hypnotics and zopiclone promote sleep, both having residual effects the following day including sleepiness and reduced alertness. In contrast, the non-benzodiazepine hypnotics zolpidem and zaleplon have no significant next-day residual effects when taken as recommended. Research on the effects of wakefulness-promoting drugs on driving ability is limited. Countermeasures for excessive daytime sleepiness have a limited effect. There is a need for a social awareness program to educate the public about the potential consequences of various sleep disorders such as narcolepsy, sleep apnea, shift-work-related sleep loss, and excessive daytime sleepiness in order to reduce the number of sleep-related traffic accidents.

Biofuel handling, pretreatment and traffic use in China

This paper describes the development situation of biofuel in China and the research progress and application in transportation and aviation area, including several key technologies of biofuel production: biofuel pretreatment and handling. This paper is aiming to find the best storing, transmitting, feeding and pretreating methods of various materials, as well as a comparison among the advantages and disadvantages of different pretreatment methods, which is expected to reduce cost in production process and reach the maximized benefits. Meanwhile, a case study of one biomass fuel production factory in China is presented with evaluation and analysis on their technology application.