Here we’re going to analyze the most famous lines in Hamlet – the “to be or not to be”
monologue. Read over this speech in act 3 of HAMLET.
In this speech, Hamlet actually makes TWO appeals to logic as he tries to persuade his audience
to accept a certain conclusion. I’ll make it bit easier for you to outline them: both lines of
reasoning are deductive and one of them is embedded within the other such that the Conclusion
of one syllogism is repeated as a minor premise that will allow our melancholy reasoner to
complete the other syllogism.
This is still confusing for many of you, so some more hints are probably in order. The famous
line (“To be or not to be”) is actually a very vague way to state the major premise of an appeal
to Modus Tollendo Ponens. Go ahead and paraphrase this vague line to clarify what Hamlet is
talking about here (ie, what the two options on the table are).
Now we know (from our Lecture Note on Deduction) that the minor premise must deny or in
some way negate one of the two terms in the major premise. So we need to be able to rule out an
option, right? That way we can conclude that the other option – the non-ruled-out one – is the
one we need to accept. But what if we can’t rule out an option right off the bat? What if we need
to deduce a statement that denies or somehow negates one of the options presented in the major
Some of you have seen this before. Have you seen the movie, The Princess Bride?
If you scroll down to the bottom of this document, I’ll jog your memory:
You need to write a short essay that begins with an intro-paragraph that does the usual things: it
should fully spell out the rhetorical situation that your speaker is confronting. It should introduce
a classical strategy for rhetorically manipulating audiences. And it should narrow your more
Follow this up with 2 or 3 body-paragraphs in which you
a) explain how one specific form of appeal to logos works;
b) analyze (by outlining) Hamlet’s appeal to this form of logos as he tries to
persuade his audience;
c) critique the strengths and/or weaknesses of the attempt to use logic in this
Assignment Length: 300 words
“To be or not to be” hamlet speech
Northampton Community College Managing Human Capital Discussion Forum
Northampton Community College Managing Human Capital Discussion Forum.
Module 6: Discussion Forum
60 unread replies.
A key to effective leadership is communication. There are many communication models that a leader can take advantage of, though some of these models can also create barriers of communication for employees. Some considerations to minimize the effect of communication barriers are involvement of employees, understanding employees’ needs, providing facts and consequences, put everything in writing, use multiple channels of communication, and, say it and say it again.
Identify the communication strategies used by your organization to enhance human capital.
Briefly explain and evaluate the communication strategy and offer suggestions on how the plan can be improved upon.
Be sure to post an initial, substantive response of at least 300 words using proper APA formatting with at least ONE scholarly reference from the course materials and respond to 2 peers attached below with substantive responses must be 100 words per peer response and include a scholarly source. A substantive initial post answers the question presented completely and/or asks a thoughtful question pertaining to the topic. Substantive peer responses address two of the following bullets:
Answer a question (in detail) posted by another student or the instructor.
Provide extensive additional information on the topic.
Explain, define, or analyze the topic in detail.
Share an applicable personal experience.
Provide an outside source (for example, a website) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA format).
Make an argument concerning the topic.
Northampton Community College Managing Human Capital Discussion Forum
Interpersonal Communications Project 4
online homework help Interpersonal Communications Project 4. Conflict Management In this assignment, you will analyze an interpersonal conflict. This conflict can be an actual situation that you have participated in or observed or from a conflict situation that you watched in a film or a television show. In your analysis, you will need to: Step One: Describe the conflict situation. Describe the characters involved. Provide a brief description of the background of the conflict. Relay the conflict conversation and outcome. Step Two: Analyze and describe the conflict by identifying what has happened in the different stages of the conflict: Source Beginning Middle End Aftermath Step Three: Describe reasons or sources of the conflict such as incompatible goals, scarce resources, or interference. Step Four: Regardless of the outcome, describe the strategies that either party or both people involved could have used to more effectively manage the conflict. Submission Details: Provide your answers in a 3- to 4-page Microsoft Word document. Cite all sources using APA format on a separate page. Submit your document to the Submissions Area by the due date assigned.Interpersonal Communications Project 4
MGT University of Phoenix Week 3 Impact of Covid 19 on Companies Responses
MGT University of Phoenix Week 3 Impact of Covid 19 on Companies Responses.
1st ResponseTwo company’s that come to mind for me is Fedex and UPS. They are logistic companies that specialize in delivering good. When Covid really hit and the borders were closed and shipping halted they had to somewhat change their strategies. They were doing business but not on the large scale they may have been accustomed to. The larger deliveries seem like they generated the most money. They had to change the way the did business ans see how they can still survive in this economy.The technology they had was for scanning items for delivery. But what happen when only essential deliveries were allow to be made. The healthcare section became huge for this company because medicine, gloves, mask and medical supplies need to be delivered to get the hospitals stocked. I truly believed they never thought that healthcare would play a huge part in the the companies growth. I have even seen reports when the warehouses were housing the medical supplies to make sure they can be delivered without delay and without shortage.I am curious to see how they will handle the holiday flu season. Not only will they have to deliver the medical supplies but they will also have to meet the demand of the season that is coming. They will have to make sure their technology and their workers are able to handle the rapid change in the economy. They will not be able to be bias in the information they withhold at this time because it will come out in the news.2nd ResponseIn the insurance company that I currently work to help with call volume and and increase online capabilities the company decided to allow our members to have access to cancel their renters policies without having to call in to do so. The logic was this would give the member the ability to cancel the renters policy if they were purchasing a new home or just no longer required to maintain the policy. Of course the pros and cons were weighed before the ability was implemented. After 3 months of having this ability online the numbers of new issues versus cancellations seemed skewed. Normally we have less cancels of the renters policies and more issues month over month. After those 3 months the cancellations were increasing significantly even though the new issues were holding steady. The company felt that since so many online abilities were a benefit to the company and worked in the past this one would be successful as well. One thing they did not take into account is that when a member needs to change the policy from one address to another we just change thee address on the policy. With the ability to cancel the policy online members were canceling the current policy and issuing a new policy so it was creating false churn. After reviewing the numbers and finding out what the issue was it was agreed to pull option to cancel from online and go back to having to speak to a representative to cancel the policy. What has worked in the past may not necessarily be the best idea for all transactions.
MGT University of Phoenix Week 3 Impact of Covid 19 on Companies Responses
Drinking Regime Evaluation of Boluses
Drinking Regime Evaluation of Boluses. Ondrej Hanu; Daniel Bro, Milan Šimko, Branislav Glik, Miroslav Jurek, Michal Rolinec, Robert Herke Slovak University of Agriculture in Nitra, Slovak Republic Original Paper Drinking regime evaluation with continuous ruminal monitoring boluses The aim of this study was to continuously monitored drinking regime of 7 dairy cows of Holstein breed using boluses during 24 weeks of lactation in relation to the outside temperature and observed daily drinking regime with the impact of drinking on rumen temperature at University Experimental Farm in Oponice. Animals were fed once daily and milked 3 times per day. The bolus pH and temperature values implemented via esophagus were measured every 15 minutes (96 data points per day) with accuracy ±0.1 ph and °C. Outside temperature by FREEMETEO meteorological server (48 times per day) was measured. Outside temperature can affect the drinking regime of dairy cows. During lactation weeks with higher outside temperature higher average number of drinking events (ANDE) was determined. The biggest difference between weeks in ANDE 18.33% (p=0.000) was found. Daily ANDE 9.25±1.85 and average daily temperature (ADT) 19.03±5.19 °C were observed. The most of the drinking events (NDE) concentrated to 4 main peaks (25.17%) during working hours (74.98%) was found. After the feed intake and milking the highest frequencies of NDE were observed. The highest average ruminal temperature after drinking (ARTAD) during night before first feeding due to lower NDE in this time were found. Overall ARTAD 36.86 °C was observed. The most measured ruminal temperatures after drinking (RTAD) (51.53%) in the interval 35 – 37 °C were found. This research proved that continuous ruminal monitoring with boluses is an appropriate tool for drinking regime evaluation and heat stress determination in herd of dairy cows. Keywords: bolus, rumen, temperature, water intake, outside temperature Water supplies for both humans and livestock are becoming a subject of increasing importance. Indeed, climate change and drinking water deficits in certain areas have meant that supplies of clean water for livestock are becoming problematic, at least during certain periods of the year. Water is considered the most important nutrient for health and performance in dairy herds. Loss of water from the body occurs through milk production, urine and fecal excretion, sweat and vapour loss from lungs (NRC, 2001). A adequate water intake is essential to avoid negative effects on animal health, performance and welfare (Murphy,1992; Meyer et al., 2004), and 25 and 50% restriction of drinking water relative to ad libitum intake decreased feed intake and milk yield in dairy cows (Steiger Burgos et al., 2001). Results of several experiments showed that an average of 83% of the water demand is met by drinking (NRC, 2001). Many studies found the association between water intake and outside temperature and between water intake and the number of drinking events (Matarazzo et al., 2003; Brown-Brandl et al., 2006; Arias et al., 2008). Drinking activity can be monitored continuously and simultaneously for randomly enrolled cows using a data acquisition system based on an individual radio frequency identification collar (Cardot et al., 2008) or with observers (Jago et al., 2005). Huzzey et al. (2005) monitored drinking activity of dairy cows using video cameras connected to a video multiplexer and a time-lapse videocassette recorder. Bewley et al. (2008) monitored ruminal temperature using boluses permanently residing in the cow’s reticulum and indentified temperatures influenced by drinking events. The aim of this study was to monitored drinking regime of dairy cows using boluses during lactation in relation to the outside temperature, daily drinking regime and the impact of drinking on rumen temperature. 2.1 Animals and housing Measured data from 7 dairy cows of Holstein breed (average age 3.57) in cooperation with the University Experimental Farm in Oponice during 24 lactation weeks were collected. Selected cows had average milk production 10 175 kg per lactation with 3.94% of fats, 3.10% of crude proteins and 4.70% of lactose. Experimental cows were housed in the groups with another dairy cows together. 2.2 Feeding and water availability Animals were fed once daily with Total Mix Ratio (Table 1) ad libitum between 4:00 and 5:00 and milked 3 times per day at 6:00, 12:00 and 18:00. Corn silage (pH 3.85) and alfalfa silage acidity (pH 4.85) with Sodium Bicarbonate (550 g.head-1) and Magnesium Oxide (51 g.head-1) were neutralised. In one section for 20 dairy cows two drinkers were available. Table 1 Total Mix Ratio composition DM (kg) NEL (MJ.kg-1) CP (%) NDF (%) Starch (%) 25.45 153.86 15.74 24.35 25.39 abbreviations: DM – dry mater, NEL – netto energy of lactation, CP – crude protein, NDF – neutral detergent fiber 2.3 Data measuring and data collecting Every dairy cow had implemented farm bolus for continual data measuring which was implemented through esophagus orally with the use of special balling gun. The bolus pH and temperature values were measured every 15 minutes (96 data points per day) with accuracy ±0.1. Outside temperature by FREEMETEO meteorological server (48 times per day) was measured. Used boluses (eCow Devon, Ltd., Great Britain) are characteristic with its small dimensions (135 – 27 mm) and weight 207 g. Data with the handset with antenna and dongle connected with USB dongle connector with the radio frequency 434 MHz in the milking parlour were downloaded. Collected data were summarized with HathorHBClient v. 1.8.1. 2.4 Statistical evaluation Statistical evaluation with IBM SPSS v. 20.0 was realised. Descriptive statistics with One-way ANOVA were recalculated. Statistically differences between average daily outside temperatures (ADT), average ruminal temperatures after drinking (ARTAD) and average numbers of drinking events (ANDE) with post hoc Tukey Test were determined. Effect of outside temperature on number of drinking events with Pearson correlation coefficient (r) was realised. As drinking event a decrease in ruminal temperature less than -0.70% and ruminal pH less than 0.00% with previous data point using data filter was selected. Drinking regime of dairy cows during lactation with average temperatures during drinking events in the Figure 1 are shown. ANDE during monitored period 9.25±1.85 and ADT 19.03±5.19 were observed. Minimal reported ANDE found Jago et al. (2005) 5.2. Higher average ANDE for monitored period observed Huzzey et al. (2005) 9.5±0.4 and Perera et al. (1986) 9.4. Cardot et al. (2008) determined ANDE 7.3±2.8 during their experiment. The effect of ADT r=0.132 on ANDE was determined (p=0.001) but in 19 cases the same change – both increase or decrase in the comparison with previous week between ANDE and ADT was found. González Pereyra et al. (2010) found effect of outside temperature on ANDE r=0.507 (p<0.05). Totally an average decrease in ANDE -4.87±5.38% and ADT -21.31±15.23% in comparison with previous week for 9 weeks was determined. The average increase in ANDE 4.68±4.24% and ADT 22.12±15.49% compared to previous week for 10 weeks was observed. Between lactation weeks statistically significant differences were determined (p=0.000). Between 4th and 5th lactation a rise in ANDE 9.84% and ADT 17.64% were found. After that ADT fall by -8.91% and in ANDE -3.36% in the 6th lactation week was determined. In the 7th week of lactation a gain in ANDE 1.19% and ADT 39.76% (p=0.000) and in the 8th lactation week a drop in ANDE -0.28% and ADT -10.87% was observed. In 9th lactation week a distinct development between ANDE (-1.02%) and ADT ( 2.93%) was detected. Statistically significant difference between 10th and 12th lactation week in ANDE -17.17% was found. After that a fall in ANDE -10.44% and ADT -27.77% (p=0.000) in 13th lactation week was determined. In 14th,15th and 16th lactation week a different development between ANDE (-3.89%; -0.13%; 6.07%) and ADT ( 0.09%; 37.19%; -1.94%) was observed again. Statistically significant difference between 14th and 15th week of lactation was found (37.19%; p=0.000). During 19th lactation week a decrease in ANDE -2.17% and ADT -26.15% (p=0.000) was found. After that in 20th and 21st lactation week an increase in ANDE and ADT was observed again. In ANDE it was 1.85% and 1.09%; in ADT 37.19% (p=0.000) and 5.56%. Statistically significant difference between 12th and 22nd lactation week -18.21% in ANDE was determined. In 22nd lactation week decreased ANDE -15.77% and ADT -33.77% (p=0.000). This decrease by another increase in ANDE 3.26% and ADT 18.26% in 23rd (p=0.000) lactation week was fallowed. In 24th and 25th lactation week a statistically significant differences in ANDE in comparison with 12th lactation week -17.87% (p=0.028) and -18.33% (p=0.020) and a decrease in ANDE -2.75%; -0.56% and in ADT -7.46%; -24.43% (p=0.000) were found. In 26th lactation week an another rise in ANDE 3.98% and ADT 35.87% (p=0.000) was observed. This rise with another fall in ANDE -0.55% and ADT -49.92% (p=0.000) was replaced. Figure 1 Drinking regime and average daily outside temperature during 18 weeks of lactation abbreviations: ADT – average daily outside temperature; ANDE – average number of drinking events Temperatures measured after drinking and number of drinking events in Figure 2 are shown. Only 0.85% of RTAD in the interval over 40 °C were found. The 2nd smallest group 5.34% of RTAD in the interval under 35 °C was determined. In the interval 39 – 40 °C were 6.99% of RTAD and in the interval 38 – 39 °C it was 14.93%. The most of RTAD in the temperature interval 37 – 38 °C and 35 – 37 °C were found. The interval 37 – 38 °C from 20.36% of RTAD and 35 – 37 °C from 51.53% of RTAD was consisted. After comparison of number of drinking events during day 4 peaks were determined. First peak in the time of 1st feeding at 5:00 was found (380). After 1st milking at 6:00 a sudden decrease -26.58% in NDE was found. Between 1st and 2nd milking NDE oscillated from 247 to 289 and represented 31.18% of NDE during the day. Next peak of NDE after 2nd milking at 13:00 was found (381). After that a sudden decrease -33.07% at 14:00 and -34.12% at 15:00 of NDE was found again. Then a linear rising trend of NDE before 3rd milking was determined. NDE increased by 23.81% at 16:00 and 39.81% at 17:00. After the 3rd milking at 18:00 another rise 21.38% in NDE and 3rd at 19:00 and 4th peak at 21:00 of NDE was found. After the highest peak at 21:00 a rapid decrease -40.87% at 22:00 and -33.91% at 23:00 in NDE was observed. This decrease continued to 3:00 when NDE fall down by -1.32% (0:00), -30.00% (1:00), -26.67% (2:00) and -18.18% (3:00). The biggest difference in NDE 83.80% between 3:00 and 21:00 was determined. It can be state that dairy cows during night drink less. Only 12.78% of NDE between 22:00 and 3:00 was realised. On the other side during 4 peaks (5:00, 13:00, 19:00 and 21:00) 25.17% of NDE was found. This fact means that dairy cows drink water mainly after feeding and milking and during night is water intake low. During experiment 74.98% of NDE during working hours and 25.02% out of working hours were determined. Cardot et al. (2008) found 2 main and 3 smaller consumption peaks and 72.70% of NDE per day during working hours and 27.00% during night on the farm was achieved. Osborne et al. (2002) claims that 25.00 of NDE during night was realised. NDE occurred during the whole day but NDE peaks were in relationship with feeding and milking (Nocek and Braun, 1985; Osborne, 2002). Figure 2 Ruminal temperatures measured after drinking and number of drinking events during day abbreviations: RTAD – ruminal temperature after drinking; NDE – number of drinking events ARTAD are shown in the Table 2. Weak correlation -0.178 (p=0.000) between ARTAD and NDE was found. Gasteiner et al. (2009) found in their experiment average ruminal temperatures from 38.12±0.80°C to 38.55±0.83°C. Bodas (2014) found ruminal temperatures from 34.57°C to 39.78°C with average 38.77 °C. Bewley et al. (2008) found dramatic decrease 9.2±0.2°C of ruminal temperature after intake of cold water. ARTAD during day 36.86 °C was found. Higher ARTAD between 0:00 and 4:00 were found. This is the result of low water intake during hours before first feeding (only 12.78% of NDE was between 22:00 and 3:00). After feeding at 5:00 and 1st milking at 6:00 a slight decrease in ARTAD -0.81% and -1.35% was observed. This decrease can be attributed to higher NDE 293.65% at 4:00 and 53.25% at 5:00. After that change from -0.62% to 0.70% in ARTAD between 1st and 2nd milking was found. After 2nd milking a slight fall by -0.03% at 13:00 and -0.35% at 14:00 was determined. This small decrease is result of higher NDE ( 41.64%) after 2nd milking. Lower NDE by -34.12% in comparison with NDE at 14:00 caused at 15:00 and 16:00 higher ARTAD by 0.37% and 0.13%. Before 3rd milking change -0.21% at 17:00 was found. Return from the milking parlour caused higher NDE. After 3rd milking a small decrease in ARTAD -0.10% at 19:00, -0.39% at 20:00 and -0.01% at 21:00 in comparison with previous hours was found. Last hours of a day changes between previous hours in ARTAD fluctuated from 0.43% to -0.20% at 22:00 and 23:00. The maximal difference 2.53% in ARTAD between 1:00 and 6:00 was observed. Table 2 Average ruminal temperature after drinking event Different letters in the columns indicate significant differences. The mean difference is significant at the 0.05 level (Tukey Test); abbreviations: S.D. – standard deviation, Cv – Coefficient of variation, Min. – minimal value, Max. – maximal value Outside temperature can affect the drinking regime of dairy cows. The weak linear relationship between ADT and NDE r=0.132 was determined (p=0.001) but in 19 cases the same change – both increase or decrease in the comparison with previous week between NDE and ADT was found. During lactation weeks with higher outside temperature higher NDE was determined. The biggest difference between weeks in NDE 18.33% (p=0.000) was found. Daily NDE 9.25±1.85 and ADT 19.03±5.19°C were observed. The most of the NDE concentrated to 4 main peaks (25.17%) during working hours (74.98%) was found. After the feed intake and milking the highest frequencies of NDE were observed. The highest ARTAD during night before first feeding due to lower NDE in this time were found. Overall ARTAD 36.86°C was found. The most measured RTAD (51.53%) in the interval 35 – 37°C were found. This research proved that continuous ruminal monitoring with boluses is an appropriate tool for drinking regime evaluation and heat stress determination in herd of dairy cows. Acknowledgments The project was supported by the Slovak National Scientific Grant Agency VEGA, Grant No. 1/0723/15. References Arias, R., Mader, T. and Escobar, P. (2008) Climatic factors affecting cattle performance in dairy and beef farms. Archivos de Medicina Veterinaria, vol. 40, no. 1, pp. 7-22. doi:10.4067/s0301-732×2008000100002 Bewley, J. M. et al. (2008) Impact of Intake Water Temperatures on Reticular Temperatures of Lactating Dairy Cows. Journal of Dairy Science, vol. 91, no. 10, pp. 3880-3887. doi:10.3168/jds.2008-1159 Bodas, R. et al. (2014) Ruminal pH and temperature, papilla characteristics, and animal performance of fattening calves fed concentrate or maize silage-based diets. Chilean Journal of Agricultural Research, vol. 74, no. 3, pp. 280-285. doi:10.4067/s0718-58392014000300005 Brown-Brandl, T. M. et al. (2006) Comparison of heat tolerance of feedlot heifers of different breeds. Livestock Science, vol. 105, no. 1, pp. 19-26. doi:10.1016/j.livsci.2006.04.012 Burgos, M. S. et al. (2001) Effect of water restriction on feeding and metabolism in dairy cows. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, vol. 280, no. 2, pp. 418-427. CARDOT, V., LE ROUX, Y. and JURJANZ, S. (2008) Drinking Behavior of Lactating Dairy Cows and Prediction of Their Water Intake. Journal of Dairy Science, vol. 91, no. 6, pp. 2257-2264. doi:10.3168/jds.2007-0204 Gasteiner, J. et al. (2009) Measuring rumen pH and temperature by an indwelling and wireless data transmitting unit and application under different feeding conditions. In Papers Presented at the 4th European Conference on Precision Livestock Farming, Wageningen 6-8 July 2009. Wageningen Academic Pub, pp. 127-133. González Pereyra, A. V. et al. (2010) Influence of Water Temperature and Heat Stress on Drinking Water Intake in Dairy Cows. Chilean Journal of Agricultural Research, vol. 70, no. 2., pp 328-336. doi:10.4067/s0718-58392010000200017 Huzzey, J. M., von Keyserlingk, M. A. G. and WEARY, D. M. (2005) Changes in Feeding, Drinking, and Standing Behavior of Dairy Cows During the Transition Period. Journal of Dairy Science, vol. 88, no. 7, pp. 2454-2461. doi:10.3168/jds.s0022-0302(05)72923-4 JAGO, J. G. et al. (2005) The drinking behaviour of dairy cows in late lactation. In 65th Conference of the New Zealand Society of Animal Production, Christchurch 21-24 June 2005. New Zealand: NZ Society Animal Production, pp. 153 – 156. Matarazzo, S. V. et al. (2003) Water Intake and Behavior of Dairy Cows in Response to Environmental Conditions. In Fifth International Dairy Housing Proceedings, Forth Worth, 29-31 January 2003. American Society of Agricultural and Biological Engineers, pp 213-217. doi: 10.13031/2013.11624 Meyer, U. et al. (2004) Investigations on the water intake of lactating dairy cows. Livestock production science, vol. 90, no. 2, pp. 117-121. doi:10.1016/j.livprodsci.2004.03.005 Murphy, M. R. (1992) Water metabolism of dairy cattle. Journal of dairy science, vol. 75, no. 1, pp. 326-333. doi:10.3168/jds.S0022-0302(92)77768-6 National Research Council (2001). Nutrient Requirements of Dairy Cattle, 7th. rev. ed. Proceedings of National Academy Sciences, Washington,D.C. Nocek, J. E. and Braund, D. G. (1985) Effect of feeding frequency on diurnal dry matter and water consumption, liquid dilution rate, and milk yield in first lactation. Journal of Dairy Science, vol. 68. no. 9, pp. 2238-2247. doi:10.3168/jds.S0022-0302(85)81096-1 Osborne, V. R., Hacker, R. R., and McBride, B. W. (2002) Effects of heated drinking water on the production responses of lactating Holstein and Jersey cows. Canadian journal of animal science, vol. 82, no. 3, pp. 267-273. doi:10.4141/A01-055 Perera, K. S. et al. (1986) Effect of season and stage of lactation on performance of Holsteins. Journal of Dairy Science, vol. 69, no. 1, pp. 228-236. doi:10.3168/jds.S0022-0302(86)80390-3 ï›ï€ªï Corresponding Author: Ondrej Hanušovský. Slovak University of Agriculture in Nitra, Faculty of Agrobiology and Food Resources, Department of Animal Nutrition, Trieda A. Hlinku 2, 949 76 Nitra, Slovak Republic e-mail: [email protected] Drinking Regime Evaluation of Boluses