Geriatric Trauma

Worldwide, the proportion of elderly people is constantly increasing. The aging of the baby boomers (people born between 1946 and 1964) and longer life spans (the maximum number of years that a human can live) result in a substantial increase in the number and proportion of older adults (whose age is ≥65). The older population is projected to more than double from 40.3 million in 2010 to 83.7 million in 2050 and, by 2050, it is estimated that older adults will represent 20.9% of the US population. In the early twentieth century, the average life expectancy at birth was 47.3 whereas it was 76.9 in 2000. With the increase in life expectancy due to improvement in quality of medical care, additionally, the oldest old age (age ≥ 85) forms a rapidly growing group within the older population. The rapid growth of these populations has many significant impacts on public health, emergency room visits, and economy

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

Removal of calcium hydroxide pastes containing N-Methyl-2-Pyrrolidone, local anaesthesia, glycerine, and methylcellulose from artificial radicular grooves: An in-vitro study

Objective: To compare the removal of calcium hydroxide (CaOH) pastes containing N-Methyl-2-pyrrolidone (NMP), lidocaine, glycerine, methylcellulose, or water from artificially created grooves. Methods: In this study, 115 human single-rooted maxillary incisors with single and straight root canals were prepared using a rotary file up to size 40/.04 and split longitudinally. A longitudinal groove was created from 2 to 5 mm from the apex and filled with CaOH combined with different vehicles. The specimens were divided among 5 experimental groups according to the vehicle as follows: distilled water, lidocaine, glycerine, methylcellulose, and NMP. The two halves were re-attached, and the canals were flushed with 10 ml of 17% EDTA for 60 seconds. The residual amount of CaOH was scored using a stereomicroscope at 8x magnification. Statistical significance was set at P<0.05. Results: The NMP-based group exhibited significantly less residual medicament compared to the distilled water (P0.05). Conclusion: The vehicle is an important factor in the successful removal of CaOH medicament from the root canals. Within the limitations of the present study, the NMP-based CaOH medicament exhibited better removal efficacy than the distilled water. However, the cleaning success of the methylcellulose-, lidocaine-, and glycerine-based groups was similar to that of distilled water

The effects of anemia in pregnancy on the mode of delivery and newborn

Objective: The aim of this study is to evaluate the effects of anemia in pregnancy on the mode of delivery and new-born. Methods: Between June and October 2009, 307 pregnant women were evaluated in terms of hemoglobin (Hb) and hematocrit (Hct) values, and delivery mode retrospectively. And also, the first and fifth minute Apgar scores, birth weight, and the values of Hb, Hct, and bilirubin, which obtained from the cord blood of neonates, were analyzed. Pregnant women were divided into two groups and classified as: hemoglobin value under 11.1 g / dl as anemic and the others as non-anemic group. In addition, the anemic group were divided into three group in terms of hemoglobin value, as follows: Group 1: 10.1 -11 mg/dl, group 2: 9.1 - 10 mg/dl, and group 3: <9 mg/dl. Results: In the study, 146 pregnants were anemic, while the 161 were non-anemic. The rate of low birth weight neonates was significantly higher in anemic pregnant women (p=0.029). The values of Hb (p=0.026) and Htc (p=0.006) were found to be lower in the anemic pregnant’ neonates. The incidence of low birth weight was significant increased when the maternal Hb value was smaller than 10g/dl (62.5% sensitivity, 74.7% specificity). Conclusion: It is observed that the low birth weight and the low values of Hb and Hct were more common in anemic pregnant neonates. Therefore, anemia should be screened and treated during the pregnancy due to the potential negative consequences

Assessment of palliative approach in the pain management in endodontic emergencies during Covid-19 outbreak: Retrospective cohort study

Aim: During the coronavirus disease, a palliative approach was recommended for the management of endodontic emergencies. This retrospective cohort study was conducted to investigate the effectiveness of dexamethasone or ibuprofen-acetaminophen combination for pain management in endodontic emergencies. Material and Methods: One hundred and eight records of patients who presented to the emergency department with dental pain were evaluated retrospectively. Since interventional procedures were not performed during the pandemic period, Specific analgesics/antibiotics for the management of pain were preferred. A follow-up protocol with a questionnaire was developed to observe the effectiveness of palliative treatment and make changes if necessary. All participants received a questionnaire to rate the pain levels 6, 12, 18, 24, 48, and 72 hours after taking the drug. All data were collected from the patient file and assessed. After inclusion and exclusion criteria, 32 patients were included (n = 19, ibuprofen + acetaminophen; n = 13, dexamethasone). Data were analyzed using the chi-square test (P = 0.05). Results: In both groups, a significant decrease in pain was experienced immediately after medication and at 6, 12, and 18 hours, with no significant difference (P > .05). However, dexamethasone (Group II) resulted in lower pain levels than ibuprofen\acetaminophen (Group I) at 24 and 48 hours (P < .05) Discussion: Both dexamethasone and ibuprofen-acetaminophen can be good palliative choices in endodontic emergencies in pandemic conditions. However, at 24 and 48 hours, dexamethasone resulted in lower pain levels

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

Transaction selection policy in tier-to-tier SBSRS by using deep q-learning

This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average