Search Results (1,624)

Purpose: Proximal femur fractures (PFFs) in elderly patients lead to decreased productivity. Skilled nursing facilities (SNFs) and inpatient rehabilitation facilities (IRFs) are non-home destinations for post-discharge disposition. This study aims to evaluate the loss of independence (LOI) following PFFs and examine the economic impact it entails. Method: The literature from various databases was collected and analyzed retrospectively. The inclusion criteria included patients age > 18 years and articles published after 1990. All studies were screened, a PRISMA chart was used to demonstrate the search process, and 24 studies were finally used for review. Results: LOI following PFFs significantly increases with age. Fractures in geriatrics avail a significant amount of post-care resources and had longer lengths of stay. Furthermore, six pre-operative risk factors were identified for non-home disposition, including age > 75, female, non-Caucasian race, Medicare status, prior depression, and Charlson Comorbidity Index. Patients discharged directly to home have lower total costs compared to those discharged to rehabilitation units. Loss of independence increases with advancing age. Conclusions: PFFs can lead to a serious loss of independence among elderly patients. Female gender, advancing age, white population, co-existing morbidities, lack of proper care, post-operative infections, limitation in mobility following surgery, and impaired cognitive function following surgery are the factors that contribute to the decline in the rate of appropriate recovery following surgery. Therefore, these factors could necessitate permanent residence in a nursing facility (IRFs and SNFs), with a direct impact on economic, social, psychological aspects and the healthcare system. Full article

Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage. Full article

Climate change restricts and alters the distribution range of plant species. Predicting potential distribution and population dynamics is crucial to understanding species’ geographical distribution characteristics to harness their economic and ecological benefits. This study uses Dendrocalamus brandisii as the research subject, aiming to accurately reveal the impact of climate change on this plant. The findings offer important insights for developing practical conservation and utilization strategies, and guidance for future introduction and cultivation. The MaxEnt model was optimized using regularization multiplier (RM) and feature combination (FC) from the ‘Kuenm’ package in R language, coupled with ArcGIS for modeling 142 distribution points and 29 environmental factors of D. brandisii. This article explored the key environmental factors influencing the potential suitable regions for D. brandisii, and predicted trends in habitat changes under SSPs2.6 and SSPs8.5 climate scenarios for the current era, the 2050s, 2070s, and 2090s. (1) The results show that when FC = QPH and RM = 1, the AUC = 0.989, indicating that the model prediction is accurate with the lowest complexity and overfitting. The key environmental factors affecting its primary suitable distribution, determined by jackknife training gain and single-factor response curve, are the precipitation of warmest quarter (bio18), the temperature seasonality (bio4), the minimum average monthly radiation (uvb-4), and elevation (Elev), contributing 93.6% collectively. It was established that the optimal range for D. brandisii is precipitation of warmest quarter of between 657 and 999 mm, temperature seasonality from 351% to 442%, minimum average monthly radiation from 2420 to 2786 J/m²/day, at elevation from 1099 to 2217 m. (2) The current potential habitat distribution is somewhat fragmented, covering an area of 92.17 × 10⁴ km², mainly located in southwest, south, and southeast China, central Nepal, southern Bhutan, eastern India, northwestern Myanmar, northern Laos, and northern Vietnam. (3) In future periods, under different climate scenario models, the potential habitat of D. brandisii will change in varying degrees to become more fragmented, with its distribution center generally shifting westward. The SSP8.5 scenario is not as favorable for the growth of D. brandisii as the SSPs2.6. Central Nepal, southern Bhutan, and the southeastern coastal areas of China have the potential to become another significant cultivation region for D. brandisii. The results provide a scientific basis for the planning of priority planting locations for potential introduction of D. brandisii in consideration of its cultivation ranges. Full article

The rapid advancement of machine learning techniques has led to their widespread application in various domains, including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging a Temporal Convolutional Network (TCN), for snowmelt forecasting of the Hindu Kush Himalayan (HKH) region. To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Transformer models. Furthermore, nested cross-validation (CV) was used with five outer folds and three inner folds, and hyperparameter tuning was performed on the inner folds. To evaluate the performance of the model, the Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), R square ($R^2$), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) were computed for each outer fold. The average metrics revealed that the TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, $R^2$ of 0.991, KGE of 0.992, and NSE of 0.991 for one-day forecasts of streamflow. The findings of this study demonstrate the effectiveness of the proposed deep learning model as compared to traditional machine learning approaches for snowmelt-driven streamflow forecasting. Moreover, the superior performance of this TCN highlights its potential as a promising deep learning model for similar hydrological applications. Full article

Free-ranging nilgai antelope (Boselaphus tragocamelus) are an understudied species, both on their native ranges of India, Pakistan, and Nepal and on their introduced ranges in southern Texas. Basic data related to population sizes, survival, reproduction, and recruitment are needed throughout their range to inform management and conservation decisions. We collected nilgai fetuses from 3 ranches in southern Texas, including East Foundation’s El Sauz and Santa Rosa ranches, and the Norias Division of the King Ranch^® from 2018–2021. We calculated the percentage of individuals that were pregnant in each of the sample years and overall. We determined monthly average, maximum, and minimum fetus length. Of 488 nilgai cows, we found 386 to be pregnant (79%) and 214 to be pregnant with twins (56%). We found nilgai cows as young as 1-year old to have fetuses and therefore to have reached sexual maturity. Sex ratios of fetuses during any sampling year did not differ. We found ample evidence supporting our hypothesis that nilgai are fecund on their introduced range of southern Texas. To prevent nilgai overpopulation and associated problems, harvest management strategies should be implemented, specifically on nilgai cows. Full article

Escherichia coli in untreated milk leads to economic losses from subclinical mastitis and reduced milk production, while also posing a public health risk due to the emergence of antimicrobial resistant strains, particularly associated with consuming unpasteurized milk and dairy products. This study aimed to determine the prevalence and antimicrobial resistance (AMR) of E. coli isolated from buffalo milk in Siddarthanagar Municipality of Rupandehi district, Nepal. A total of 102 milk samples were collected from lactating buffaloes. The isolation and identification of E. coli were carried out using enrichment media, selective media, and biochemical tests. Antimicrobial susceptibility testing was carried out using the Kirby–Bauer disk diffusion method on Mueller–Hinton agar (Merck), according to the Clinical and Laboratory Standards Institute (CLSI) recommendations. Resistance was tested against gentamicin, amikacin, ciprofloxacin, enrofloxacin, ceftriaxone, ceftazidime, cotrimoxazole, and chloramphenicol. In addition to this, farmers were administered a questionnaire consisting of both open- and close-ended questions to identify various animal-related and management-related risk factors associated with the prevalence of E. coli. The prevalence of E. coli in our study was 29.4% (n = 30/102). Ceftriaxone and ceftazidime showed 100% resistance, while cotrimoxazole and amikacin showed 86.7% and 80% resistance, respectively. Furthermore, 86.7% of E. coli isolates were multidrug resistant (MDR). Despite suggestive trends, associations between E. coli prevalence and risk factors lacked statistical significance, necessitating further research. While some antibiotics exhibited effectiveness, many faced resistance, highlighting the need for prudent antimicrobial usage and increased awareness among farmers. Raising awareness about milk pasteurization and implementing food safety practices is essential for ensuring farmers and public health. Full article

This research study assesses the effectiveness of the Socio-Technical Assistance (STA) program when combined with owner-driven housing reconstruction on rural private housing recovery after Nepal’s 2015 earthquake, particularly regarding vulnerable households. Through a quantitative, 304-question survey, the study reveals that 96% of households credited STA activities for accelerating reconstruction, with 95% acknowledging its significance and 78% emphasizing its necessity. Notably, 89% expressed dependency on STA for reconstruction, and 85% believed it heightened disaster risk reduction awareness. In conclusion, the study establishes that STA activities significantly contributed to the successful reconstruction of houses for vulnerable households, addressing such critical aspects as financial support, technical assistance, housing accessibility, earthquake-resilient construction, improved livelihoods, and safety enhancements. The field study presents crucial recommendations for enhancing the effectiveness of Socio-Technical Assistance (STA) activities in post-earthquake, rural private housing reconstruction. Emphasizing the need for tailored, demand-driven interventions, the study cautions against relying solely on an owner-driven reconstruction model, challenging the one-size-fits-all strategy. The study proposes integrating tailored interventions into overarching recovery strategies, advocating for coordinated efforts to enhance disaster risk reduction (DRR) awareness and to cultivate resilient communities in particularly vulnerable households as aligned with United Nations Sustainable Development Goal 11, which focuses on sustainable cities and communities. This research aims to enhance the literature on post-disaster humanitarian shelter and settlement by emphasizing the significance of inclusive and comprehensive approaches to recovery and reconstruction. Full article

The article describes in detail the equipment and method for measuring the Doppler frequency shift (DFS) on an inclined radio path, based on the principle of the phase-locked loop using an SDR receiver for the investigation of seismogenic and man-made disturbances in the ionosphere. During the two M7.8 earthquakes in Nepal (25 April 2015) and Turkey (6 February 2023), a Doppler ionosonde detected co-seismic and pre-seismic effects in the ionosphere, the appearances of which are connected with the various propagation mechanisms of seismogenic disturbance from the lithosphere up to the ionosphere. One day before the earthquake in Nepal and 90 min prior to the main shock, an increase in the intensity of Doppler bursts was detected, which reflected the disturbance of the ionosphere. A channel of geophysical interaction in the system of lithosphere–atmosphere–ionosphere coupling was traced based on the comprehensive monitoring of the DFS of the ionospheric signal, as well as of the flux of gamma rays in subsoil layers of rocks and in the ground-level atmosphere. The concept of lithosphere–atmosphere–ionosphere coupling, where the key role is assigned to ionization of the atmospheric boundary layer, was confirmed by a retrospective analysis of the DFS records of an ionospheric signal made during underground nuclear explosions at the Semipalatinsk test site. A simple formula for reconstructing the velocity profile of the acoustic pulse from a Dopplerogram was obtained, which depends on only two parameters, one of which is the dimension of length and the other the dimension of time. The reconstructed profiles of the acoustic pulses from the two underground nuclear explosions, which reached the height of the reflection point of the sounding radio wave, are presented. Full article

Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards. Full article

Measuring the flexural strength of restorative materials such as zirconia is crucial for providing proper indications for clinical applications and predicting performance. Great variations in specimen preparation for flexural strength measurements exist among laboratories. The aim was to evaluate how the processing method, surface treatment, and test method of the specimens affect the flexural strength of zirconia. Zirconia specimens (VITA YZ HT) (n = 270) were processed using CAD/CAM or were conventionally milled with three different surface treatments (machined, ground, polished) and were measured with three-point bending (non-chamfered/chamfered) or biaxial flexural strength test. Weibull statistics were conducted. The mean flexural strength values ranged from 612 MPa (conventional, machined, three-point bending non-chamfered) to 1143 MPa (CAD/CAM, polished, biaxial flexural strength). The highest reliability is achieved when specimens are prepared using thoroughly controllable processing with CAD/CAM and subsequently polished. Higher strength values are achieved with the biaxial flexural strength test method because the stress concentration in relation to the effective volume is smaller. Polishing reduces surface microcracks and therefore increases the strength values. Full article

A response of global seismic activity to the geomagnetic storms of St. Patrick’s Day (March 17) in 2013 and 2015 is investigated. These two storms occurred during nearly identical storm sudden commencement times and similar solar flux levels. We have revealed a rather similar pattern of the most substantial earthquakes that have occurred since these storms. Two major crust continental earthquakes, in Iran (M = 7.7), 16 April 2013, and in Nepal (M = 7.8), 25 April 2015, have occurred with a time delay of ~30 and ~39 days after geomagnetic storm onsets in 2013 and 2015, respectively. After that, the great and major deep-focused earthquakes occurred beneath the Sea of Okhotsk (M = 8.3, 24 May 2013, Russia) and the Pacific Ocean (M = 7.8, 30 May 2015, Japan) with a time delay of ~68 and ~74 days, respectively. Geomagnetic storm onsets occurred at 06:04 UT in 2013 and 04:48 UT in 2015. At this time, the high latitudinal areas of the longitudinal regions, in which the mentioned earthquakes occurred in the future, were located under the polar cusp, where the solar wind plasma has direct access to the Earth’s environment. An analysis of the remaining ten earthquakes with M ≥ 7.5, which occurred around the globe in 2013 and 2015, proved the above findings that seismic activity may respond to geomagnetic storm onset with a time delay from some days to some months. Full article

The present research focuses on optimizing 5G base station deployment and visualization, addressing the escalating demands for high data rates and low latency. The study compares the effectiveness of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimizer (GWO) in both Urban Macro (UMa) and Remote Macro (RMa) deployment scenarios that overcome the limitations of the current method of 5G deployment, which involves adopting Non-Standalone (NSA) architecture. Emphasizing population density, the optimization process eliminates redundant base stations for enhanced efficiency. Results indicate that PSO and GA strike the optimal balance between coverage and capacity, offering valuable insights for efficient network planning. The study includes a comparison of 28 GHz and 3.6 GHz carrier frequencies for UMa, highlighting their respective efficiencies. Additionally, the research proposes a 2.6 GHz carrier frequency for Remote Macro Antenna (RMa) deployment, enhancing 5G Multi-Tier Radio Access Network (RAN) planning and providing practical solutions for achieving infrastructure reduction and improved network performance in a specific geographical context. Full article

Citrus fruit cultivation, especially mandarin oranges, is crucial to the economy of Nepal’s hilly regions due to their ideal geoclimatic conditions. Despite its economic importance, the sector faces several challenges, such as inadequate grafting techniques, low-quality saplings, and ineffective post-harvest storage. This paper explores these issues and proposes innovative solutions through the use of Internet of Things (IoT) technology. To address these challenges, we identified key areas for improvement. First, we focused on extending grafting practices during the off-season to ensure a higher success rate and better-quality saplings. Second, we examined different post-harvest storage methods to determine their effectiveness in terms of shelf life, decay loss, and quality of fruit. In addition to exploring post-harvest strategies, this paper provides preharvest recommendations for farmers, emphasizing methods to enhance fruit quality and longevity through effective pre-storage practices. Our IoT-based approach introduces off-season grafting in polyhouses and advanced monitoring for post-harvest storage. The results are promising: We achieved grafting success rates of 91% for acid lime and 92% for local mandarin orange varieties. Additionally, our research compared different post-harvest storage methods for mandarin oranges, including room, cellar, and cold chamber. We assessed these methods based on shelf life, physiological weight loss, and the total soluble solids (TSS) to titratable acidity (TA) ratio. The cold chamber proved to be the most effective method, offering superior conditions for storing mandarin oranges. The IoT-based monitoring system played a crucial role in maintaining optimal temperature, humidity, and gas content within the cold chamber, resulting in reduced post-harvest losses and extended shelf life. These findings highlight the transformative potential of IoT technology in mandarin orange cultivation and post-harvest storage. Full article

This paper introduces a dynamic consensus-based economic dispatch (ED) algorithm utilizing the Alternating Direction Method of Multipliers (ADMM) to optimize real-time pricing and generation/demand decisions within a decentralized energy management framework. The increasing complexity of modern energy markets, driven by the proliferation of Distributed Energy Resources (DER) and variable demands from hybrid electric vehicles, necessitates a departure from traditional centralized dispatch methods. This research proposes a novel ADMM-based solution tailored for non-responsive and responsive demand units that integrates demand response mechanisms to adaptively manage real-time fluctuations while enhancing security and privacy through distributed data management. The testing of the algorithm on the IEEE 39 bus system under various load conditions over 24 h demonstrated the algorithm’s effectiveness in handling traditional and renewable energy sources, particularly highlighting the economic benefits of shifting controllable loads to periods of low-cost renewable availability. The findings underscore the algorithm’s potential to reduce energy costs, enhance energy efficiency, and offer a scalable solution across diverse grid systems, contributing significantly to advancing global energy policy and sustainable management practices. Full article

Poly(vinyl acetate) nanofibers doped with PdS-ZnS nanoparticles (PdS-ZnS/PVAc nanofibers) were fabricated via an electrospinning technique. PdS-ZnS nanoparticles were in situ synthesized by adding (NH₄)₂S solution to poly(vinyl acetate)/zinc acetate/palladium acetate solution. Electrospinning of the formed colloidal solution led to the formation of poly(vinyl acetate) nanofibers containing uniformly distributed PdS-ZnS nanoparticles. The prepared samples were characterized by field emission scanning electron microscopy, X-ray diffraction, transmission electron microscopy and Fourier transform infrared spectroscopy. In photocatalytic activity investigation, the PdS-ZnS/PVAc nanofibers showed remarkably enhanced performance towards water photosplitting under solar irradiation compared to the ZnS/PVAc nanofibers. This enhanced performance is attributed to the synergistic effects of heterostructured PdS-ZnS nanoparticles, which can improve photogenerated charge migration and solar light absorption. Full article