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Acute effects of static stretching (SS) and proprioceptive neuromuscular facilitation (PNF) on local and non-local range of motion (ROM) were assessed in 29 participants. Three evaluations were performed one week apart: week-1 Control session (CS); weeks 2–3 either SS or PNF interventions (randomized). Dominant and non-dominant limbs, local (hamstring extensibility) and non-local ROMs (Shoulder extension-ShE) were collected at baseline (T0), immediately after (T1), and fifteen minutes post-intervention (T2). No differences were found between time-points during the CS. Local-ROM significantly increased (p=0.0002, ES=0.74 and 0.0079, 0.56, for dominant and non-dominant lower limbs, respectively) after both SS and PNF. No interaction between time and treatment was detected for ShE in both limbs. However, post-hoc analysis revealed a significant increase in dominant upper limb ShE between T0 and T1 only after SS (p=0.002; +6.5%). Acute bouts of SS and PNF can increase local-ROM, however, no clear effects were observed for non-local ROM. [ABSTRACT FROM AUTHOR]

Context: The Buffalo Concussion Treadmill Test (BCTT) is a standard assessment of exercise tolerance utilized for exercise prescription following concussion and to inform decisions regarding return to play. One limitation of the BCTT is that interpretation of test results is dependent on individuals' self-report of symptom exacerbation with exertion. Symptoms following concussion are significantly underreported or unreported. Combining objective neurocognitive assessment with exercise tolerance testing may enable clinicians to objectively identify those requiring further assessment or rehabilitation before return to play. The purpose of this study was to investigate how performance on a neurocognitive assessment battery is affected by provocative exercise testing. Design: Prospective cohort study, pretest/posttest. Methods: A total of 30 participants included 13 women (43.3%), age 23.4 (1.93) years, height 173.56 (10) cm, weight 77.35 (16.3) kg, and 11 (36.7%) with history of concussion. All participants completed a neurocognitive assessment battery, including the Stroop Test and standardized assessments of working memory, attention, and information processing speed/accuracy in single-task (seated position) and dual-task conditions (walking on a treadmill at 2.0 miles per hour). The neurocognitive assessment battery was performed at baseline and after the standard BCTT test protocol. Results: BCTT: Average percentage of heart rate maximum (%HRmax) = 93.97% (4.8%); average maximum rating of perceived exertion = 18.6 (1.5). Time-based performance in single-task and dual-task conditions significantly improved from baseline (P <.05) following maximal exercise testing on the BCTT for the following neurocognitive assessments: concentration-reverse digits, Stroop congruent, and Stroop incongruent. Conclusions: Healthy participants demonstrated improvements across multiple domains of neurocognitive performance following the exercise tolerance testing on the BCTT. Understanding normal responses in neurocognitive performance for healthy individuals following exercise tolerance testing may allow clinicians to more objectively monitor the trajectory of recovery following sports-related concussion. [ABSTRACT FROM AUTHOR]

Copyright of Archives of the Turkish Society of Cardiology / Türk Kardiyoloji Derneği Arşivi is the property of KARE Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: When resources are extracted and used by society, they are not necessarily lost for future generations. Therefore, recent publications on impact assessment of abiotic resource use in life cycle assessment focus on a decreased accessibility of resources due to dissipation, rather than depletion. In a previous study, dissipation was defined as a function of the global change in accessible stock due to human actions, and the global amount of the accessible stock, assuming a very long-term time perspective (more than 500 years). In this paper, a short-term time perspective (25 years) is adopted. Methods: The same generic characterization model is used, but different choices are outlined to derive characterization factors for a short-term perspective (25 years). To illustrate how the short term might be approached, a preliminary set of characterization factors is developed, based on assumptions and estimates. Results: The problem of resource use is defined as follows: the decrease of accessibility on a global level of primary and/or secondary elements over the short term due to the net result of compromising actions (i.e., emissions, dissipation in the technosphere, occupation in use, and exploration for new stocks). Characterization factors are derived based on assumptions, like the following: the accessible stock is based on present estimates of accessible stocks in the environment and the technosphere; estimates of accessible stocks in the technosphere are based on past extractions and generic recycling rates; all flows that are presently not recycled are assumed to be inaccessible. Finally, weighting between elements and the functions they have for the present society is based on the added value of the economic sector that is affected due to the decreased accessibility. Discussion and conclusion: A preliminary set of characterization factors is proposed for 55 elements. They assess the impact of the present use of resources on the decreased accessibility in the short term due to emissions and dissipation in the technosphere. However, calculation of impact category scores is still hampered by a lack of appropriate data for dissipative flows in life cycle inventory databases. The presented calculations are based on several simplifications and proxies. A more detailed distinction of dissipative flows and estimates of stocks in the technosphere may be possible based on (dynamic) SFA modelling of elements in different applications. To derive a more mature set of characterization factors, it is recommended to use the presented model as a basis and further elaborate or replace the proxies. [ABSTRACT FROM AUTHOR]

A business model, defined as the underlying logic of a company, is not permanent over time. Instead, companies need to introduce innovations in the business model to be competitive in the market. This is even more critical for family firms which are continuously looking for a way to obtain sustainable growth, together with satisfactory performance. The objective of this research is to analyze the impact that business model innovation activities have in the short/long-term growth and performance of family firms. This study was obtained from panel data made up of 112 valid responses from family firms involved in business model innovation. A composite model approach has been used for data analysis. The significant components of BMI that lead to greater growth and performance are identified, the distinction between long-term and short-term effects of BMI on those outcomes is made, and BMI is proven as an authoritative source of competitive advantage and growth in family firms. Those aspects are valuable insights for researchers and managers with regards to which innovation propositions help sustain growth and performance over time.

This paper proposes a novel hybrid methodology for short-term forecasting of hourly. The F-Multi Criteria Decision Making-Neural Network (F-MCDM-NN) hybrid model is designed to forecast Global Horizontal Irradiance (GHI) 1 day ahead based on Meteorological data for the target photovoltaic generation. The proposed hybrid modeling is based on meteorology data i.e. wind speed, wind direction, pressure, rainfall, temperature, humidity, gust, and visibility, data especially for optimizing the operation of power generating electricity from photovoltaic (PV) generation system. This modeling is a hybrid of Fuzzy-Multi Criteria Decision Making (F-MCDM) algorithm modeling and Neural Network (NN) model. The novelty of this combination model is taking into account the meteorology data. A set of data on GHI was from the PV station in Indonesia, which is used as research test data. The first hybrid model implements F-MCDM as an input data preprocessing technique prior to the NN method. The error statistical indicators of the F-MCDMNN hybrid model using the Mean Absolute Percentage Error (MAPE) is 7.5% and the Root-Mean- Square Error (RMSE) is 73.7 W/m². The hybrid model prediction is then compared to measured data and simulation results indicate that the F-MCDM-NN-based hybrid model presented in this research can calculate hourly GHI with satisfactory accuracy with better result compared to the Neural Network method. [ABSTRACT FROM AUTHOR]

A business model, defined as the underlying logic of a company, is not permanent over time. Instead, companies need to introduce innovations in the business model to be competitive in the market. This is even more critical for family firms which are continuously looking for a way to obtain sustainable growth, together with satisfactory performance. The objective of this research is to analyze the impact that business model innovation activities have in the short/long-term growth and performance of family firms. This study was obtained from panel data made up of 112 valid responses from family firms involved in business model innovation. A composite model approach has been used for data analysis. The significant components of BMI that lead to greater growth and performance are identified, the distinction between long-term and short-term effects of BMI on those outcomes is made, and BMI is proven as an authoritative source of competitive advantage and growth in family firms. Those aspects are valuable insights for researchers and managers with regards to which innovation propositions help sustain growth and performance over time. [ABSTRACT FROM AUTHOR]

As one key environmental regulation measure, China's pollution levy policy has been widely discussed; however, existing research has solely concentrated on the emissions reduction effect of pollution levy policies in the short term but has ignored long-term effectiveness, which may cause research bias. Thus, taking pollution levy standard adjustments as the core independent variable, this study builds firm-level pollution data and proves that the pollution levy policy is effective both in the short and long term. Furthermore, it demonstrates that the emissions reduction effect is achieved mainly by decreasing production and increasing the removal of sulfur dioxide ( S O 2 ). In addition, it is uncovered that the emissions reduction effect is mainly a result of two processes—the first is the greater use of clean energy and higher energy efficiency that can cause a decrease in the production of S O 2 , and the second is the utilization efficiency of waste gas treatment facilities, which can increase the removal of S O 2 . [ABSTRACT FROM AUTHOR]

Abstract This article studies the intricate dynamics of family resilience within the Chinese productivist regime over the course of seven decades, emphasizing the instrumental role of public policy in shaping this resilience. Drawing on a dynamic approach, the research demonstrates how, across distinct periods of central planning and economic reform, policies have persistently harnessed families to advance economic objectives. Showing that the productivist regime’s double-edged role on family resilience emerged while public policies bolstered short-term family resilience in times of sustained economic growth and favorable demographics, and signs of family sector downturn—declining fertility rates, family dysfunctions—became evident, the current article underscores the challenges of a model that primarily perceives families as economic instruments. Advocating for a policy paradigm shift that harmonizes economic ambitions with family well-being, this study offers valuable insights for policy-makers and lays the groundwork for future research in comparative welfare systems.

Considering that wind power is proportional to the cube of the wind speed variable, which is highly random, complex power grid management tasks have arisen as a result. Wind speed prediction in the short term is crucial for load dispatch planning and load increment/decrement decisions. The chaotic intermittency of speed is often characterised by inherent linear and nonlinear patterns, as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. In this study, wavelet transform (WT), autoregressive integrated moving average (ARIMA), extreme gradient boosting trees (XGBoost), and support vector regression (SVR) are combined to predict high-resolution short-term wind speeds obtained from three Southern African Universities Radiometric Network (SAURAN) stations: Richtersveld (RVD); Central University of Technology (CUT); and University of Pretoria (UPR). This hybrid model is termed WT-ARIMA-XGBoost-SVR. In the proposed hybrid, the ARIMA component is employed to capture linearity, while XGBoost captures nonlinearity using the wavelet decomposed subseries from the residuals as input features. Finally, the SVR model reconciles linear and nonlinear predictions. We evaluated the WT-ARIMA-XGBoost-SVR’s efficacy against ARIMA and two other hybrid models that substitute XGBoost with a light gradient boosting machine (LGB) component to form a WT-ARIMA-LGB-SVR hybrid model and a stochastic gradient boosting machine (SGB) to form a WT-ARIMA-SGB-SVR hybrid model. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and prediction interval normalised average width (PINAW), the proposed hybrid model provided more accurate and reliable predictions with less uncertainty for all three datasets. This study is critical for improving wind speed prediction reliability to ensure the development of effective wind power management strategies.

Abstract Plants are crucial because they give us food and oxygen. With the idea of living on other planets and taking long trips in space, we need to understand and explore the way how plants can survive in these strange places. However, while the gravity response on earth’s surface has been extensively studied in plants, in space, where the gravity is very weak, things get confusing. One of the intriguing and essential subjects for space life is understanding how plants can sustain themselves in microgravity conditions. To investigate this, various clinostat devices and the CRISPR/Cas9 technique are crucial tools for exploring the functioning of PIN-formed protein and related signal transduction pathways. In this review, we aim to provide researchers with a brief overview of the mechanisms of CRISPR/Cas9, which can be immensely helpful when using this method alongside clinostat machines. Our primary goal in this review is to address the missing gaps in existing literatures, focusing on how plants perceive gravity and experimental approaches applicable for studying their responses to microgravity, both on earth and in space.

Background and objective: The possible negative impact of radical surgery on patients’ health-related quality of life (HRQoL) plays an important role in preoperative counseling. Here, we analyzed the HRQoL of patients treated for upper urinary tract urothelial carcinoma (UTUC) in the context of a single-arm phase 2 multicenter study, in which the safety and efficacy of a single preoperative intravesical instillation with mitomycin C were investigated. Our objective was to investigate early changes in HRQoL in patients undergoing radical surgery for UTUC and identify factors associated with these outcomes. Methods: Patients with pTanyN0-1M0 UTUC were prospectively included. HRQoL was assessed using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 (EORTC QLQ-C30) questionnaire at baseline, and at 1 and 3 mo after surgery. A linear mixed model was used to evaluate the changes in HRQoL over time and identify the variables associated with these outcomes. The clinical effect size was used to assess the clinical impact and level of perceptibility of HRQoL changes for clinicians and/or patients based on given thresholds. Key findings and limitations: Between 2017 and 2020, 186 patients were included. At baseline, 1 mo after surgery, and 3 mo after surgery, response rates were 91%, 84%, and 78%, respectively. One month after surgery, a statistically significant and clinically relevant deterioration was observed in physical, role, and social functioning, and for the included symptom scales: constipation, fatigue, and pain. An improvement in emotional functioning was observed. At 3 mo, HRQoL returned to baseline levels, except emotional functioning, which improved at 1 mo and persisted to be better than that before surgery. Age >70 yr was associated with worse physical functioning, but better social and emotional functioning. Male patients reported better emotional functioning than females. Postoperative complications were negatively associated with social functioning. Conclusions and clinical implications: UTUC patients treated with radical surgery experienced a significant, albeit temporary, decline in HRQoL. Three months following surgery, HRQoL outcomes returned to baseline levels. This information can be used to counsel UTUC patients before undergoing radical surgery and contextualize recovery after surgery. Patient summary: We investigated the changes in quality of life as reported by patients who underwent surgery for upper tract urothelial carcinoma (UTUC). We found that patients experienced a decline in quality of life 1 mo after surgery, but this was temporary, with full recovery of quality of life 3 mo after surgery. These findings can help doctors and other medical staff in counseling UTUC patients before undergoing radical surgery.

Considering that wind power is proportional to the cube of the wind speed variable, which is highly random, complex power grid management tasks have arisen as a result. Wind speed prediction in the short term is crucial for load dispatch planning and load increment/decrement decisions. The chaotic intermittency of speed is often characterised by inherent linear and nonlinear patterns, as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. In this study, wavelet transform (WT), autoregressive integrated moving average (ARIMA), extreme gradient boosting trees (XGBoost), and support vector regression (SVR) are combined to predict high-resolution short-term wind speeds obtained from three Southern African Universities Radiometric Network (SAURAN) stations: Richtersveld (RVD); Central University of Technology (CUT); and University of Pretoria (UPR). This hybrid model is termed WT-ARIMA-XGBoost-SVR. In the proposed hybrid, the ARIMA component is employed to capture linearity, while XGBoost captures nonlinearity using the wavelet decomposed subseries from the residuals as input features. Finally, the SVR model reconciles linear and nonlinear predictions. We evaluated the WT-ARIMA-XGBoost-SVR's efficacy against ARIMA and two other hybrid models that substitute XGBoost with a light gradient boosting machine (LGB) component to form a WT-ARIMA-LGB-SVR hybrid model and a stochastic gradient boosting machine (SGB) to form a WT-ARIMA-SGB-SVR hybrid model. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and prediction interval normalised average width (PINAW), the proposed hybrid model provided more accurate and reliable predictions with less uncertainty for all three datasets. This study is critical for improving wind speed prediction reliability to ensure the development of effective wind power management strategies. [ABSTRACT FROM AUTHOR]

Copyright of Revista Técnica Energía is the property of Centro Nacional de Control de Energia CENACE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Commercial aviation experienced exceptional growth in the 2010s, marked by the growing importance of its activity in Asia. This trend collapsed in 2020 and 2021 with the Covid crisis. The resulting chaos has brought to light a different geography of the airline world. Underlying this evolution are the more or less permissive and hesitant policies of different countries. In 2022, the pre-Covid trends again are obvious, but the Chinese giant is still missing. The market thus appears resilient as long as political decisions on health matters no longer hinder it. Better still, airlines have used the crisis to accelerate their rationalization. However, the general uncertainties appear more and more numerous and diverse.

This study aimed to investigate the association between objective baropodometric and radiological measurements and patient self-reported functional outcomes, assessed through the Knee Injury and Osteoarthritis Outcome Score (KOOS). Additionally, it sought to evaluate the effectiveness of static baropodometry in predicting short-term KOOS results following unilateral total knee arthroplasty (TKA). We conducted a prospective single-center study involving 32 patients who underwent unilateral TKA for knee osteoarthritis (KOA). Patients were evaluated both preoperatively and six months postoperatively, utilizing objective measurements derived from static baropodometric analysis in a normal, relaxed, bipedal standing position using a multi-platform Plantar Pressure Analysis System (PPAS) and radiographic measurements of the femorotibial angle (FTA) and subjective assessments through the national validated version of the KOOS. The study found an insignificant average correction of −0.69° ± 4.12° in the preoperative FTA at the sixth month after TKA. Moreover, there were no significant differences in the KOOS based on different types of knee alignment (KA) both pre- and postoperatively (p > 0.05). No significant correlations were observed between the KOOS, and total average affected and unaffected plantar pressures (TAAPP and TAUPP) pre- and postoperatively, as well as KA pre- and postoperatively. However, significant changes were observed in TAAPP and TAUPP measurements after unilateral TKA. TAAPP demonstrated a significant increase postoperatively (mean change (SD) = 18.60 (47.71); p = 0.035). In conclusion, this study found no significant correlation between KA, static baropodometric measurements, including pre- and postoperative differences, and KOOS outcomes. Therefore, static plantar pressure measurements alone might not serve as a reliable predictor of short-term clinical outcomes after unilateral TKA, as reported by patients. [ABSTRACT FROM AUTHOR]

Background: stroke is considered a major cause of disability worldwide. Vitamin D has been linked to cardiovascular health, including the reduction of stroke risk, while its role in stroke outcome has not been fully studied yet, so we aimed to find the role of vitamin D in the prediction of short-term outcomes of ischemic stroke. Methods: This prospective cohort study was conducted on 58 patients with their first acute ischemic stroke who underwent assessment of their serum vitamin D and their functional outcome three months later by modified Rankin scale. Results: serum vitamin D had a significant role in the prediction of poor short-term outcomes. The mean vitamin D level in our patients was 14.01 ± 8.94ng/ml. Conclusions: the poor short-term outcome of a stroke at three months can be predicted by the presence of hypovitaminosis D, so vitamin D supplementation should be taken into consideration when managing ischemic stroke patients. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers: The Interdisciplinary Journal of Study Abroad is the property of Frontiers Journal, Inc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PurposeTo evaluate the possible risk factors of opaque bubble layer (OBL) formation in small incision lenticule extraction (SMILE) surgery and its effects on visual quality.MethodsFifty-six eyes from 28 patients were included in this study. The preoperative parameters and intraoperative designs were recorded. Corneal high-order aberrations (HOAs), point spread function (PSF), and modulation transfer function (MTF) were measured using iTrace at pre-operation, 1 week, 1 month, and 3 months after SMILE. Generalized Estimating Equation and Linear Mixed Effects Model were employed for statistical analysis.ResultsThe mean OBL area in SMILE surgery was 2.75% ± 1.25%. The patients were divided into groups based on whether the OBL was greater than the mean group or less than the mean group. Compared to the group with a smaller OBL area, the group with the larger OBL area had steeper corneal curvature and thinner cap thickness, the OBL area was positively correlated with the preoperative keratometry (r = 0.21, p = 0.04) and preoperative spherical value (r = 0.47, p = 0.01). The group with the larger OBL area induced more corneal SA and trefoil at 1 week postoperatively, but the difference was not significant at 1 month and 3 months postoperatively.ConclusionA steep corneal curvature, thin cap thickness, and high preoperative spherical value are possible risk factors for OBL formation in SMILE surgery. The OBL increased the ocular and corneal HOAs postoperatively for a short period (1 week), while it did not affect the long-term outcomes.

Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models. [ABSTRACT FROM AUTHOR]

Background: Revision total hip arthroplasty (THA) is a challenging procedure that burdens the healthcare system. Despite being associated with worse outcomes relative to its primary counterpart, postoperative mortality after revision THA remains ill-defined. The present study aimed to (1) establish the overall 30-day mortality rate after revision THA and (2) explore the mortality rate stratified by age, comorbidity burden, and aseptic versus septic failure. Methods: The American College of Surgeons – National Surgical Quality Improvement Program (ACS-NSQIP) database was retrospectively reviewed for all patients who underwent revision THA from 2011 to 2019. A total of 23,501 patients were identified and grouped into mortality (n = 161) and mortality-free (n = 23,340) cohorts. Patient demographics, comorbidities, and aseptic/septic failure were evaluated. Results: The overall 30-day mortality was 0.69%. The mortality rate by age group (normalised per 1000 patients) was 0 (18–39 years [Y]), 0.67 (40–49 Y), 1.10 (50–59 Y), 2.58 (60–69 Y), 6.15 (70–79 Y) 19.32 (80–89 Y), and 58.22 (90+Y) (p < 0.001). The mortality rate by ASA classification (normalised per 1000 patients) was 0 (ASA I), 1.47 (ASA II), 6.94 (ASA III), 45.42 (ASA IV), and 200 (ASA V) (p < 0.001). The 30-day mortality rate for the septic and aseptic cohorts was 1.03% and 0.65%, respectively (p = 0.038). CCI scores (p < 0.001), diabetes (p < 0.001), systematic sepsis (p < 0.001), poor functional status (p < 0.001), BMI < 24.9 kg/m2 (p < 0.001), and dirty/infected wounds (p < 0.001) were all associated with increased mortality risk. Conclusions: 1 in 145 patients will suffer mortality during the 30 days after revision THA. PJI-related revision THA was associated with 1.5-fold increase in 30-day mortality rate compared to its aseptic counterpart. Certain patient determinants and baseline comorbidities, as measured by ASA and CCI scores, were associated with higher 30-day mortality rates. Therefore, it is imperative to identify such risk factors and implement perioperative patient optimisation pathways to mitigate the risk among vulnerable patients. [ABSTRACT FROM AUTHOR]

A few reports have demonstrated the efficacy of middle meningeal artery embolization (MMAE) alone for mildly symptomatic chronic subdural hematoma (CSDH); however, the clinical course in the early posttreatment period remains unclear. The purpose of this study was to analyze the short-term outcomes of this technique at our center. This study was based on a retrospective analysis of a single-center consecutive case series. Patients with mildly symptomatic CSDH treated with MMAE alone between July 2020 and June 2022 were examined. Neurological examinations and head computed tomography scans were performed before treatment and 1, 7, 14, and 28 days after treatment. The clinical course of the patients was analyzed. In particular, symptom improvement within 1 week from treatment or rescue evacuation and the factors associated were evaluated. Fifteen patients were included in this study. No procedure-related complications occurred. Partial or complete recovery within the first week from treatment was observed in 10 cases (66.7%), and the symptoms resolved completely in a median of 26 (6.5–33.5) days. Rescue evacuation was needed in 3 cases (20.0%). The hematoma volume and midline shift gradually decreased from baseline, with a significant improvement within the first week (P = 0.030 and 0.0032, respectively). MMAE alone provides relatively early improvement in cases of mildly symptomatic CSDH and may be a potential alternative to surgical evacuation or medical therapy. [ABSTRACT FROM AUTHOR]

Background: The optimal duration of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) is not established in chronic kidney disease (CKD) patients. Objective: In this meta-analysis, we evaluated the efficacy and safety of long duration DAPT compared with short duration DAPT in CKD patients after PCI. Methods: We searched PubMed, Cochrane, and Embase to identify studies assessing the effect of DAPT duration in CKD patients with PCI. Endpoints included all-cause mortality, major adverse cardiovascular events (MACE), death or myocardial infarction (MI), revascularization, and bleeding. Event rates were compared with a random-effects model expressed by odds ratio (OR) and 95% confidence interval (CI). Results: Six studies were included. CKD patients with extended DAPT duration were at a lower risk of mortality (OR 1.40, 95% CI: 1.11–1.77), MACE (OR 1.33, 95% CI: 1.17–1.51), mortality or MI (OR 1.24, 95% CI: 1.10–1.40), and stroke (OR 1.28, 95% CI: 1.05–1.56). However, there was no significant difference in revascularization and bleeding events between the two groups. Mortality was higher in patients with dialysis or drug-eluting stent comparing short- to long-term DAPT. Conclusions: Prolonged DAPT might decrease the risk of mortality, MACE, and stroke in patients with CKD without any significant difference in bleeding or revascularization. Additional studies are required to determine whether long-term DAPT could be considered for most CKD patients after PCI. [ABSTRACT FROM AUTHOR]

Background: Lung cancer (LC) is the most common solid tumor type in the intensive care unit (ICU). This study investigated the characteristics of LC patients admitted to the ICU, the major reasons for their admission, short-term mortality, and associated risk factors. Methods: Patients with LC were retrospectively identified in the publicly available, large-scale, single-center database Medical Information Mart for Intensive Care (MIMIC) III. Demographic and clinical characteristics, including age, sex, smoking history, comorbidities, type of admission to ICU, major diagnoses, illness severity score as assessed by the Simplified Acute Physiology Score (SAPS) II and the Sequential Organ Failure Assessment (SOFA), ICU length of stay (LOS), use of mechanic ventilation (MV) or vasopressors, the existence of do-not-resuscitate (DNR) orders, and metastatic status were collected. The major reasons for ICU admission were analyzed in subgroups. The multivariate logistic regression analysis was used to determine the factors associated with the 28-day and 6-month mortality. Results: A total of 1242 ICU admissions were included. Diseases of respiratory (42.7%), nervous (14.3%), and cardiovascular (11.9%) systems accounted for the top reasons for admission. Pneumonia/pneumonitis, respiratory failure, and sepsis were the primary reasons for ICU admission. The median survival was 2.93 (95% CI: 2.42–3.43) months. The 28-day inhospital and the 6-month mortality were 30.6% and 68.2%, respectively. Sepsis (63.9%), respiratory failure (47.0%), and pleural effusion (40.9%) accounted for the top three highest 28-day ICU mortality in all causes. An age ≥ 65 years, a SAPS II ≥ 37, a SOFA ≥ 3, metastasis, and MV use were independent risk factors for an inferior 28-day survival rate, while only metastatic status and SOFA score were associated with the 6-month mortality. SAPS II was accepatable and better than SOFA in predicting 28-day ICU [area under the curve (AUC): 0.714 and 0.658, respectively] or 28-day inhospital mortality (AUC: 0.717 and 0.660, respectively). Conclusion: The 6-month prognosis for LC patients admitted to ICU was dismal. Multidisciplinary collaboration between intensivists and oncologists to identify high-risk patients and to determine a risk-benefit ratio of ICU treatment may improve survival prospects. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyze the criteria of clinical practice of provisional and temporary restorations among general dentists in Saudi Arabia and Egypt. An online survey with a total of 28 questions classified into three domains was developed. 200 general dental practitioners from two largest cities in Saudi Arabia and Egypt respectively were emailed to join the survey voluntarily and anonymously. Only general dentists after internship year, appointed in two major central governmental hospital in each of the study cities were invited. Descriptive statistics and Chi square analysis were performed to study the received responses. Recorded responses were 155. There was no general agreement between participants regarding the applied strategies of provisional/temporary restorations followed in their practice. Most of the participants are aware of the difference between both terms; temporary and provisional restoration. Moreover, adequate knowledge and skills of using temporary and provisional restorations during the undergraduate program was sufficient. However, more recommendations should be implemented to encourage temporization and provsionlization among young practitioners in different clinical situations. [ABSTRACT FROM AUTHOR]

Background: Recent studies have suggested that memory might benefit from physical exercise. Some studies also show that exercise has adverse effects on short-term memory. The number of studies on the effects of a single bout of exercise on memory is rather limited, and their results are inconsistent. In this study, we want to see the effect of a single bout of aerobic exercise on memory (short term and long term) when the learning session is before the exercise. We also want to see the effect of aerobic exercise on working memory when the learning session is after the exercise. Aims and Objectives: The aim is to find the effect of aerobic exercise on working, short-term and long-term memory. Materials and Methods: Sixty healthy medical students were divided into two groups of 30 each. Both groups were asked to listen to 20 Arabic words (proper nouns) before the experiment. Students having any knowledge of Arabic were excluded from the study. Then, one group performed high-intensity exercise on a Bicycle Ergometer for 30 min and the other group silently relaxed on a chair. The subjects were asked to repeat the words heard after 20 min of exercise and 24 h of exercise. Data were recorded manually and analyzed statistically. In another experiment, the words were listened after the exercise was over, and the subjects were asked to recall after 20 min of exercise. Unpaired t-test was applied to compare the data between the two groups, and P-value and t-value were calculated. Results: When the words were recalled 20 min after exercise, the mean number of recalled words was more in the relaxing group (RG) than in the exercise group, but the result was reverse when recall was made 24 h after the exercise, i.e., more recall by exercise group. When the learning session was after the exercise and subjects were asked to recall words 20 min after the exercise, we found more recall by RG than the exercise group. Conclusion: We conclude that aerobic exercise has an adverse effect on short-term memory and working memory but has a beneficial effect on long-term memory.

The data-driven method is a common approach to solar irradiation prediction. However, its weakness is that it is highly dependent on the characteristics of the prior data, which leads to a lag delay in forecasting. Therefore, this paper proposes a prediction model that considers data-driven, data-cycle variations, and statistical information for solving such problems. Firstly, the periodicity of the data is removed using the Fourier transform. Secondly, monthly daily characterization matrices are constructed to predict from a global perspective without including information from previous time series. Finally, the coupled autoregressive and dynamical system (CARDS) model is improved using the identity matrix. Comparing the model proposed in this paper with seven other popular prediction models, the error can be reduced by 30.2 % on average at four locations in the northern and southern hemispheres. Moreover, the proposed model does not increase the computational burden of existing models. [ABSTRACT FROM AUTHOR]

Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers' interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology has enabled accurate forecasting of electricity consumption. However, the impact of computation cost afforded by these ML models is often ignored in favour of accuracy. This study considers both accuracy and computation cost as concurrently significant factors because together they shape the technology environment as well as create economic impact. Thus, a three-fold optimized load forecasting model is proposed which includes (1) application specific parameters selection, (2) impact of different dataset granularities and (3) implementation of specific data preparation. It deploys and compares the widely used back-propagation Artificial Neural Network (ANN) and Random Forest (RF) models for the prediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operational calendars resulting in improved performance. The outcomes are indicative that the granularity i.e. the scale of details in data, and set of reduced and full input parameters impact performance accuracies differently for ANN and RF models. Experimental results show that overall RF model performed better both in terms of accuracy as well as computational time for a 1-min, 15-min and 1-h dataset granularities with the mean absolute percentage error (MAPE) of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suited for a real-time energy monitoring application. [ABSTRACT FROM AUTHOR]

Accurate wind speed prediction has been becoming an indispensable technology in system security, wind energy utilization, and power grid dispatching in recent years. However, it is an arduous task to predict wind speed due to its variable and random characteristics. For the objective to enhance the performance of forecasting short-term wind speed, this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit (GRU). The time series decomposition algorithm combines the following two parts: (1) the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and (2) wavelet packet decomposition (WPD). Firstly, the normalized wind speed time series (WSTS) are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal. The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS. Finally, GRU networks are established for all the relevant components of the signals, and the predicted wind speeds are obtained by superimposing the prediction of each component. Results from two case studies, adopting wind data from laboratory and wind farm, respectively, suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm, and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.

Background: Despite the success of reverse shoulder arthroplasty (RSA), complication rates remain high (13% to 25%), due to instability, infection, and glenoid component loosening, which can lead to revision. The aim of the present study was to report the early clinical outcomes of RSA using a new hybrid baseplate design, in comparison with the literature on other common RSA baseplates. Methods: The authors retrospectively analyzed the records of 142 patients (142 shoulders) who underwent primary RSA using a hybrid baseplate design by the senior surgeons between May 2014 and December 2018. Preoperative and postoperative assessments included the Constant score (CS) and range of motion, including active forward elevation, external rotation, and internal rotation. Results: Of the initial cohort of 142 patients, 13 were lost to follow-up (8.6%), 2 died (1.3%), and 8 required reoperations with implant removal (5.3%). The remaining 119 patients comprised 71 women (60%) and 48 men (40%), aged 73.6 ± 7.3 years at index surgery, 43 of whom required bony increased offset (36%). At a minimum follow-up of 2 years, the CS improved by 37.3 ± 16.1, active forward elevation increased by 51.2° ± 38.1°, external rotation increased by 16.4° ± 25.0°, and internal rotation increased by 1.5 ± 3.2. Conclusion: At a minimum follow-up of 2 years after RSA using a new hybrid baseplate system, the CS and range of motion were satisfactory and comparable to those in recent systematic reviews. The findings of this study suggest that this hybrid baseplate system provides satisfactory outcomes in the short term, although longer follow-up studies are needed to validate its long-term efficacy.

Pensando con-junto a diferentes imágenes y propuestas artísticas, el texto invita a reflexionar alrededor de los presentes escenarios de flexibilidad y corto plazo: la disolución de las barreras de los tiempos de trabajo y no trabajo, así como la fragmentación de la experiencia temporal. Un recorrido por algunos de losimaginarios de la inestabilidad y la precariedad temporal, física y emocional. Thinking-with different images and artistic proposals, the article invites us to reflect on the current flexibility and short-term scenarios: the dissolution of the barriers of working and non-working times, as well as the fragmentation of the temporary experience. A journey through some of the imaginaries of temporary, physical and emotional instability and precariousness.

Over recent years, rural abandonment and climate change have challenged grazed wooded landscapes in Mediterranean mountain regions. Fire hazard management procedures such as grazing, prescribed burning, or mechanical clearing must be adjusted to the new socioeconomic and environmental situation and according to the context and circumstances of each territory. This study contributes to adjusting vegetation management techniques in response to low grazing pressure by evaluating the combined effect of mechanical clearing and grazing on the structural and floristic dynamics of understory vegetation in the open-grazed Mediterranean oak forests of northern Portugal. To this end, three treatments were established: mechanical clearing with grazing, mechanical clearing without grazing, and grazing without clearing (the control). The floristic inventories were carried out using the point quadrat method, and the structure was evaluated using line transects. Herbaceous biomass was determined by destructive methods. This study shows that mechanical vegetation clearing effectively reduces phytovolume and, thus, fire hazards. However, for its effects to endure, it must be combined with subsequent grazing, which does not always occur. No consistent pattern was found in the floristic dynamics of the shrub-grassland mosaic (species richness and diversity) related to shrub-clearing or grazing over the short term (24 months). [ABSTRACT FROM AUTHOR]

Background: Kinesio taping (KT) is one of the therapeutic interventions in sports medicine practice. The study aims to assess the acute effect of different KT methods on postural control in individuals with functional ankle instability (FAI) after ankle muscle fatigue. Methods: Twenty-eight participants with FAI were recruited to complete maximum voluntary isometric contraction (MVIC) and proprioception of ankle using isokinetic dynamometer, dynamic postural control using Y-balance test and static postural control using a force platform after a fatigue protocol in four taping conditions: facilitatory KT (FKT), ankle balance taping (ABT), sham taping (ST) and no taping (NT). Results: No significant difference was observed for the data MVIC and proprioception after ankle muscle fatigue amongst the four taping treatments. A significant difference in Y-Balance Test was observed amongst the four taping treatments at posterolateral direction (p < 0.001) and posteromedial direction (p < 0.001), suggesting that KT may significantly improve dynamic postural control following ankle muscle fatigue. For Center of pressure (COP) measurements, the mediolateral COP sway range of NT was significantly larger than that of FKT (p = 0.003) and ST (p < 0.001), suggesting that the placebo effect of KT was inevitable. Conclusion: The effect of KT seems increased dynamic postural control in individuals with FAI after ankle muscle fatigue, and this effect is not strongly related to the taping methods. By preventing fatigue-related impairments of postural control, KT may help reduce the risk of injury in individuals with FAI. [ABSTRACT FROM AUTHOR]

The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a genetic algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 min water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash–Sutcliffe Efficiencies of 0.91 and 0.83, and relative root mean square errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real-time control applications, such as dynamic pressure control. HIGHLIGHTS ANN models were used for short-term (15 min) urban water demand predictions.; The hyperparameters of the ANN model were optimized with a genetic algorithm for better model performance.; The results of the ANN approach were compared to an ARIMA and a pattern-based models for two different datasets.; The performance results proved GA optimized ANN model as an efficient approach for short-term UWD predictions.;