Supply Chain Management Practices and Lean Production Improvement in Food and Beverage Industry.

The paper is focused on the implementation of Artificial Intelligence in Lean Production and Supply Chain Management. Focusing primarily on the fact that there exists a gap between the expected performance from the companies while executing the production process and the actual performance given by them. To cover that gap the research is endeavouring to analyse the impact of implementation of Artificial Intelligence in Lean Production and Supply Chain. Artificial Intelligence can be the star to hitch the wagon of lean production to. With minimised wastage, abstainment from scrappage, minimised employee costs, adaptability to variation in the voluminous production, producing varied ranges of products the major hinderances and shortcoming of the production line get solved. Although some are still skeptical about the extend to whether they should implement AI into the core production lines. As uploading company data to the cloud makes the company vulnerable to cybercrimes, technical breakdowns can disrupt and halt the entire production line. The paper goes about the process by evaulating the option from the mindset of the executives who are responsible for the production and supply line. Qualitative approach is used for collecting data and narrative and disclosure approach is applied for analysis. Thematics is used for coding the data and assigning them with values to obtain desired result

Lunar Exploration Missions Since 2006

The announcement of the Vision for Space Exploration in 2004 sparked a resurgence in lunar missions worldwide. Since the publication of the first "New Views of the Moon" volume, as of 2017 there have been 11 science-focused missions to the Moon. Each of these missions explored different aspects of the Moon's geology, environment, and resource potential. The results from this flotilla of missions have revolutionized lunar science, and resulted in a profoundly new emerging understanding of the Moon. The New Views of the Moon II initiative itself, which is designed to engage the large and vibrant lunar science community to integrate the results of these missions into new consensus viewpoints, is a direct outcome of this impressive array of missions. The "Lunar Exploration Missions Since 2006" chapter will "set the stage" for the rest of the volume, introducing the planetary community at large to the diverse array of missions that have explored the Moon in the last decade. Content: This chapter will encompass the following missions: Kaguya; ARTEMIS (Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moons Interaction with the Sun); Change-1; Chandrayaan-1; Moon Impact Probe; Lunar Reconnaissance Orbiter (LRO); Lunar Crater Observation Sensing Satellite (LCROSS); Change-2; Gravity Recovery and Interior Laboratory (GRAIL); Lunar Atmosphere and Dust Environment Explorer (LADEE); Change-3

Rapid Increases in the Steady-state Concentration of Reactive Oxygen Species in the Lungs and Heart After Particulate Air Pollution Inhalation.

In vitro studies suggest that reactive oxygen species contribute to the cardiopulmonary toxicity of particulate air pollution. To evaluate the ability of particulate air pollution to promote oxidative stress and tissue damage in vivo, we studied a rat model of short-term exposure to concentrated ambient particles (CAPs). We exposed adult Sprague-Dawley rats to either CAPs aerosols (group 1; average CAPs mass concentration, 300 +/- 60 micro g/m3) or filtered air (sham controls) for periods of 1-5 hr. Rats breathing CAPs aerosols for 5 hr showed significant oxidative stress, determined as in situ chemiluminescence in the lung [group 1, 41 +/- 4; sham, 24 +/- 1 counts per second (cps)/cm2] and heart (group 1, 45 +/- 4; sham, 24 +/- 2 cps/cm2) but not liver (group 1, 10 +/- 3; sham, 13 +/- 3 cps/cm2). Increases in oxidant levels were also triggered by highly toxic residual oil fly ash particles (lung chemiluminescence, 90 +/- 10 cps/cm2; heart chemiluminescence, 50 +/- 3 cps/cm2) but not by particle-free air or by inert carbon black aerosols (control particles). Increases in chemiluminescence showed strong associations with the CAPs content of iron, manganese, copper, and zinc in the lung and with Fe, aluminum, silicon, and titanium in the heart. The oxidant stress imposed by 5-hr exposure to CAPs was associated with slight but significant increases in the lung and heart water content (approximately 5% in both tissues, p < 0.05) and with increased serum levels of lactate dehydrogenase (approximately 80%), indicating mild damage to both tissues. Strikingly, CAPs inhalation also led to tissue-specific increases in the activities of the antioxidant enzymes superoxide dismutase and catalase, suggesting that episodes of increased particulate air pollution not only have potential for oxidant injurious effects but may also trigger adaptive responses

Engaging first year lecturers with threshold learning outcomes and concepts in their disciplines

In this paper, we report on an investigation of what students need to learn in the first year in various discipline-based subjects to launch then on their way to meet specified discipline threshold learning outcomes (TLOs) by the time they graduate. We frame our investigation using both the threshold concepts that the students must master in first year in order to succeed in learning in the discipline and also the threshold learning outcomes that they need to achieve by third year. We describe and analyse workshops used to engage lecturers with the challenges of designing first year curriculum in their r discipline, suggest why threshold concepts are useful in focusing both lecturers and students on what is essential, and outline briefly some of the creative solutions the lecturers offered

Studies on the Physicochemical and Physico-Mechanical Properties of Activated Palm Kernel Shell blended with Carbon Black filled NR Vulcanizates

Palm kernel shell was activated using chemical activation of H3PO4 and KOH. Various amounts of activated palm kernel shell (APKS) couple with carbon black (CB) and other conventional ingredients were used to produce natural rubber vulcanizates (NR vulcanizates). The NR vulcanizates were compounded on a two-row mill and tested for its physico-mechanical properties. The results for characterization of physicochemical properties carried out on APKS  were ash content (2.06%), moisture content (8.06%), %carbon (54.41%), particle size (4.00, 3.35, 2.00, 1.18mm), bulk density (0.62g/ml) and pH (5.3).The results show significant values for all, the moisture and ash content were within the recommended standard of ASTM (3-10max) and (< or =8) respectively. The filler loading concentrations CB/APKS were labeled as mixes 1 to 7. The composition of CB/APKS filler loading ratios were 30:0, 25:5, 20:10, 15:15, 10:20, 5:25, and 0:30 samples 1,2,3,4,5,6 and 7 respectively. Results obtained showed that CB/APKS filled vulcanizates exhibited improvement in the physico-mechanical properties investigated. The results obtained for CB/APKS across the samples filler loading shows that CB composition possess higher UTS, EB and rubber fatigue test while APKS filler loading composition exhibited higher hardness and young modulus. Abrasion resistance was excellent for both CB and APKS filler loading composition.Keywords: Activated Palm Kernel Shell, filler, carbon black, Chemical Activation, Natural Rubber

Designing first-year sociology curricula and practice

Many countries are now specifying standards for graduates in different disciplines, including sociology. In Australia, the Australian Sociological Association (TASA) has developed Threshold Learning Outcomes (TLOs) for sociology to provide the learning outcomes that students graduating with a bachelor’s degree in sociology should achieve. These TLOs have encouraged universities to think explicitly about their sociology curriculum in a holistic way. This paper reports on a project that investigated the skills and concepts sociology students need to learn in first year to meet the TLOs by the time they graduate. The project identified the needs of students as they transition from school or work into the study of sociology in first year through a study of literature of first-year pedagogy and a student survey. A workshop was held for sociology that involved 37 academics from 14 universities. The workshop was used to promote a rethink of teaching of sociology in the light of the new TLOs as well as to collect ideas from the participants. The student surveys, workshop ideas and relevant literature were analyzed and synthesized for each TLO to determine what skills and concepts first-year students needed to learn, identify what they might find difficult and propose strategies for teaching. The paper also provides practical ideas for engaging academics with thinking holistically about the sociology curriculum and for teaching and learning sociology in the first year of an undergraduate degree

A Machine Learning Approach to Constructing Weekly GDP Tracker Using Google Trends

[EN] The outbreak of the COVID-19 pandemic further highlighted the limitation of existing traditional indicators as policy formulation, particularly during crisis periods, demands timely and granular data. We construct the first weekly GDP tracker in the Philippines using topic- and category- based Google Trends search volumes with the aid of machine learning models. We find that our weekly GDP Tracker is a useful high-frequency tool in nowcasting economic activity, especially during periods of extreme economic duress as the trends and developments in the actual GDP growth are well-captured by the model. Our weekly Tracker was able to capture about 96 percent of the slumpobserved in actual GDP growth in Q2 2020, reflecting the tracker’s overall good performance and the potential of the use of Google Trends. The top three Google Trends searches in predicting GDPgrowth using the SHAP interpretability tool are “unemployment”, “subsidy”, and “investment”. We also showed that the machine learning-based GDP tracker outperforms the traditional autoregression models under study in terms of lower root mean square error (RMSE) for both train and test datasets. Thus, pending the availability of quarterly national accounts, our weekly GDP tracker can serve as useful complementary surveillance tool for monitoring economic activity.Armas, JC.; R. Mapa, C.; T. Guliman, MEJ.; G. Castañares, ML.; S. Centeno, GP. (2023). A Machine Learning Approach to Constructing Weekly GDP Tracker Using Google Trends. Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2023.2023.16039556