Genel Olarak Pazarlama Kavramı ve Turizm Pazarlaması

İnsanlık tarihinin başlangıcından beri insanların ihtiyaçları, istekleri ve daha fazlasına sahip olma arzuları varolagelmiştir. Tarım toplumlarında bu ihtiyaçlarını değiş-tokuş yöntemiyle gidermeye çalışan insanlar özellikle makine ve kitle üretimine geçişin sağlandığı Sanayi Devrimi ile birlikte ihtiyaçlarından daha fazlasının üretildiği bir dönemi yaşamaya başlamışlardır. Bu dönemde işletmeler; müşterileri kendilerine çekebilmek adına farklı pazarlama stratejilerini izlemeye ve farklı pazarlama anlayışlarını benimsemeye başlamışlardır. Pazarlamanın düşüncesinin gelişimine bakıldığında üretimin aşamalarıyla aynı yönde bir gelişme gösterdiği görülmektedir. Günümüzde de işletmeler ihtiyaçları çerçevesinde her geçen gün yeni bir çağdaş pazarlama anlayışını uygulamaya koymaktadırlar. Turizm hem ekonomik hemde akademik önemi ve değeri geç keşfedilen sektörlerden birisidir. Turizm alanındaki çalışmalar diğer temel alanlardan çok sonra başlasa da günümüzde aynı seviyede ilerleme yolundadır. Endüstri sektörü ve işletmeleri üzerine yoğunlaşan endüstri pazarlamasından otuz yıl kadar sonra turizm pazarlamasına da değinilmeye başlanmıştır. Bu gecikmenin arka planında ise turizmin ekonomik, toplumsal ve işletmecilik değerinin geç farkedilmesinin rolü büyüktür

Manipulation monitoring and robot intervention in complex manipulation sequences

Compared to machines, humans are intelligent and dexterous; they are indispensable for many complex tasks in areas such as flexible manufacturing or scientific experimentation. However, they are also subject to fatigue and inattention, which may cause errors. This motivates automated monitoring systems that verify the correct execution of manipulation sequences. To be practical, such a monitoring system should not require laborious programming.Peer ReviewedPostprint (author's final draft

Temporal Trends and Predictors of Antimicrobial Resistance Among \u3cem\u3eStaphylococcus\u3c/em\u3e spp. Isolated from Canine Specimens Submitted to a Diagnostic Laboratory

Background Resistance to commonly used antimicrobials is a growing concern in both human and veterinary medicine. Understanding the temporal changes in the burden of the problem and identifying its determinants is important for guiding control efforts. Therefore, the objective of this study was to investigate temporal patterns and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) between 1993 and 2009. Methods Retrospective data of 4,972 Staphylococcus isolates assessed for antimicrobial susceptibility using the disk diffusion method at the UKVDL between 1993 and 2009 were included in the study. Temporal trends were assessed for each antimicrobial using the Cochran-Armitage trend test. Logistic regression models were used to investigate predictors of antimicrobial resistance (AMR) and multidrug resistance (MDR). Results A total of 68.2% (3,388/4,972) Staphylococcus isolates were S. intermedius group (SIG), 18.2% (907/4,972) were coagulase-negative staphylococci (CoNS), 7.6% (375/4,972) were S. aureus, 5.8% (290/4,972) were S. hyicus, and S. schleiferi subsp. coagulans comprised 0.2% (12/4,972) of the isolates. The overall percentage of AMR and MDR were 77.2% and 25.6%, respectively. The highest levels of AMR were seen in CoNS (81.3%; 737/907), S. aureus (80.5%; 302/375), and SIG (77.6%; 2,629/3388). The lowest levels of AMR were observed in S. hyicus (57.9%; 168/290) and S. schleiferi subsp. coagulans (33.3%; 4/12). Overall, AMR and MDR showed significant (p \u3c 0.001) decreasing temporal trends. Significant temporal trends (both increasing and decreasing) were observed among 12 of the 16 antimicrobials covering 6 of the 9 drug classes assessed. Thus, significant increasing temporal trends in resistance were observed to β-lactams (p \u3c 0.001) (oxacillin, amoxicillin-clavulanate, cephalothin, and penicillin (p = 0.024)), aminoglycosides (p \u3c 0.001) (gentamicin, and neomycin), bacitracin (p \u3c 0.001), and enrofloxacin (p \u3c 0.001). In contrast, sulfonamide (p \u3c 0.001) (sulfadiazin) and tetracycline (p = 0.010) resistant isolates showed significant decreasing temporal trends in AMR. Staphylococcus spp., geographic region, and specimen source were significant predictors of both AMR and MDR. Conclusions Although not unexpected nor alarming, the high levels of AMR to a number of antimicrobial agents and the increasing temporal trends are concerning. Therefore, continued monitoring of AMR among Staphylococcus spp. is warranted. Future studies will need to identify local factors responsible for the observed geographic differences in risk of both AMR and MDR

Temporal trends and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to a diagnostic laboratory

Background Resistance to commonly used antimicrobials is a growing concern in both human and veterinary medicine. Understanding the temporal changes in the burden of the problem and identifying its determinants is important for guiding control efforts. Therefore, the objective of this study was to investigate temporal patterns and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) between 1993 and 2009. Methods Retrospective data of 4,972 Staphylococcus isolates assessed for antimicrobial susceptibility using the disk diffusion method at the UKVDL between 1993 and 2009 were included in the study. Temporal trends were assessed for each antimicrobial using the Cochran-Armitage trend test. Logistic regression models were used to investigate predictors of antimicrobial resistance (AMR) and multidrug resistance (MDR). Results A total of 68.2% (3,388/4,972) Staphylococcus isolates were S. intermedius group (SIG), 18.2% (907/4,972) were coagulase-negative staphylococci (CoNS), 7.6% (375/4,972) were S. aureus, 5.8% (290/4,972) were S. hyicus, and S. schleiferi subsp. coagulans comprised 0.2% (12/4,972) of the isolates. The overall percentage of AMR and MDR were 77.2% and 25.6%, respectively. The highest levels of AMR were seen in CoNS (81.3%; 737/907), S. aureus(80.5%; 302/375), and SIG (77.6%; 2,629/3388). The lowest levels of AMR were observed in S. hyicus (57.9%; 168/290) and S. schleiferi subsp. coagulans (33.3%; 4/12). Overall, AMR and MDR showed significant (p Conclusions Although not unexpected nor alarming, the high levels of AMR to a number of antimicrobial agents and the increasing temporal trends are concerning. Therefore, continued monitoring of AMR among Staphylococcus spp. is warranted. Future studies will need to identify local factors responsible for the observed geographic differences in risk of both AMR and MDR

Temporal trends and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to a diagnostic laboratory

Background Resistance to commonly used antimicrobials is a growing concern in both human and veterinary medicine. Understanding the temporal changes in the burden of the problem and identifying its determinants is important for guiding control efforts. Therefore, the objective of this study was to investigate temporal patterns and predictors of antimicrobial resistance among Staphylococcus spp. isolated from canine specimens submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) between 1993 and 2009. Methods Retrospective data of 4,972 Staphylococcus isolates assessed for antimicrobial susceptibility using the disk diffusion method at the UKVDL between 1993 and 2009 were included in the study. Temporal trends were assessed for each antimicrobial using the Cochran-Armitage trend test. Logistic regression models were used to investigate predictors of antimicrobial resistance (AMR) and multidrug resistance (MDR). Results A total of 68.2% (3,388/4,972) Staphylococcus isolates were S. intermedius group (SIG), 18.2% (907/4,972) were coagulase-negative staphylococci (CoNS), 7.6% (375/4,972) were S. aureus, 5.8% (290/4,972) were S. hyicus, and S. schleiferi subsp. coagulans comprised 0.2% (12/4,972) of the isolates. The overall percentage of AMR and MDR were 77.2% and 25.6%, respectively. The highest levels of AMR were seen in CoNS (81.3%; 737/907), S. aureus(80.5%; 302/375), and SIG (77.6%; 2,629/3388). The lowest levels of AMR were observed in S. hyicus (57.9%; 168/290) and S. schleiferi subsp. coagulans (33.3%; 4/12). Overall, AMR and MDR showed significant (p\u3c0.001) decreasing temporal trends. Significant temporal trends (both increasing and decreasing) were observed among 12 of the 16 antimicrobials covering 6 of the 9 drug classes assessed. Thus, significant increasing temporal trends in resistance were observed to β-lactams (p\u3c0.001) (oxacillin, amoxicillin-clavulanate, cephalothin, and penicillin (p = 0.024)), aminoglycosides (p\u3c0.001) (gentamicin, and neomycin), bacitracin (p\u3c0.001), and enrofloxacin (p\u3c0.001). In contrast, sulfonamide (p\u3c0.001) (sulfadiazin) and tetracycline (p = 0.010) resistant isolates showed significant decreasing temporal trends in AMR. Staphylococcus spp., geographic region, and specimen source were significant predictors of both AMR and MDR. Conclusions Although not unexpected nor alarming, the high levels of AMR to a number of antimicrobial agents and the increasing temporal trends are concerning. Therefore, continued monitoring of AMR among Staphylococcus spp. is warranted. Future studies will need to identify local factors responsible for the observed geographic differences in risk of both AMR and MDR

Manipulation monitoring and robot intervention in complex manipulation sequences

Trabajo presentado al IX Robotics Science and Systems: "Workshop on Robotic Monitoring" (RSS-WRM), celebrado en Berkeley (US) del 12 al 16 de julio de 2014.-- et al.Compared to machines, humans are intelligent and dexterous; they are indispensable for many complex tasks in areas such as flexible manufacturing or scientific experimentation. However, they are also subject to fatigue and inattention, which may cause errors. This motivates automated monitoring systems that verify the correct execution of manipulation sequences. To be practical, such a monitoring system should not require laborious programming.The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme 3, Information and Communication Technologies) under grant agreement no. 269959, IntellAct.Peer Reviewe

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson’s disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. Methods: ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. Discussion: We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.publishedVersio