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Objectives: The objectives of this paper is to conduct a bibliometric analysis to examine the research status and development trend of anterior cruciate ligament injury and reconstruction in children and adolescents over the past 20 years. Design: Descriptive Research. Methods: This study obtained information regarding studies on Anterior Cruciate Ligament Reconstruction in Children and Adolescents from the Web of Science Core Collection database. Visual and bibliometric analysis were conducted using VOSviewer, Origin 2022, Pajek64 5.18and Excel 2019. These analytic tools facilitated the analysis of various aspects, including countries/regions, institutions, authors, journals and keywords related to the research. Results: From 2003 to 2023, a total of 1328 articles were retrieved in WOS, and 637 articles were selected by two authors. The most productive institutions are Childrens Hosp Philadelphia, Kocher, ms. Their articles have the highest number of publications and citations. The American journal of sports medicine is the most frequently cited journal for articles on anterior cruciate ligament reconstruction in children and adolescents. The most common keywords used in these articles were "anterior cruciate ligament reconstruction", "injury, children, adolescent", and "skeletally immature patients". Conclusions: This study provides valuable insights into the research focus of anterior cruciate ligament reconstruction in children and adolescents. In recent years, there has been significant attention paid to areas of "the return to sport, re-repture rate and functional recovery after anterior cruciate ligament reconstruction" in this specific population. These aspects have emerged as key directions for future research in this field. [ABSTRACT FROM AUTHOR]

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors. [ABSTRACT FROM AUTHOR]

Background: Recruiting large cohorts efficiently can speed the translation of findings into care across a range of scientific disciplines and medical specialties. Recruitment can be hampered by factors such as financial barriers, logistical concerns, and lack of resources for patients and clinicians. These and other challenges can lead to underrepresentation in groups such as rural residents and racial and ethnic minorities. Here we discuss the implementation of various recruitment strategies for enrolling participants into a large, prospective cohort study, assessing the need for adaptations and making them in real-time, while maintaining high adherence to the protocol and high participant satisfaction. Methods: While conducting a large, prospective trial of a multi-cancer early detection blood test at Geisinger, an integrated health system in central Pennsylvania, we monitored recruitment progress, adherence to the protocol, and participants' satisfaction. Tracking mechanisms such as paper records, electronic health records, research databases, dashboards, and electronic files were utilized to measure each outcome. We then reviewed study procedures and timelines to list the implementation strategies that were used to address barriers to recruitment, protocol adherence and participant satisfaction. Results: Adaptations to methods that contributed to achieving the enrollment goal included offering multiple recruitment options, adopting group consenting, improving visit convenience, increasing the use of electronic capture and the tracking of data and source documents, staffing optimization via leveraging resources external to the study team when appropriate, and integrating the disclosure of study results into routine clinical care without adding unfunded work for clinicians. We maintained high protocol adherence and positive participant experience as exhibited by a very low rate of protocol deviations and participant complaints. Conclusion: Recruiting rapidly for large studies – and thereby facilitating clinical translation – requires a nimble, creative approach that marshals available resources and changes course according to data. Planning a rigorous assessment of a study's implementation outcomes prior to study recruitment can further ground study adaptations and facilitate translation into practice. This can be accomplished by proactively and continuously assessing and revising implementation strategies. [ABSTRACT FROM AUTHOR]

The increase in the concentration of geological gas emissions in the atmosphere and particularly the increase of methane is considered by the majority of the scientific community as the main cause of global climate change. The main reasons that place methane at the center of interest, lie in its high global warming potential (GWP) and its lifetime in the atmosphere. Anthropogenic processes, like engineering geology ones, highly affect the daily profile of gasses in the atmosphere. Should direct measures be taken to reduce emissions of methane, immediate global warming mitigation could be achieved. Due to its significance, methane has been monitored by many space missions over the years and as of 2017 by the Sentinel-5P mission. Considering the above, we conclude that monitoring and predicting future methane concentration based on past data is of vital importance for the course of climate change over the next decades. To that end, we introduce a method exploiting state-of-the-art recurrent neural networks (RNNs), which have been proven particularly effective in regression problems, such as time-series forecasting. Aligned with the green artificial intelligence (AI) initiative, the paper at hand investigates the ability of different RNN architectures to predict future methane concentration in the most active regions of Texas, Pennsylvania and West Virginia, by using Sentinel-5P methane data and focusing on computational and complexity efficiency. We conduct several empirical studies and utilize the obtained results to conclude the most effective architecture for the specific use case, establishing a competitive prediction performance that reaches up to a 0.7578 mean squared error on the evaluation set. Yet, taking into consideration the overall efficiency of the investigated models, we conclude that the exploitation of RNN architectures with less number of layers and a restricted number of units, i.e., one recurrent layer with 8 neurons, is able to better compensate for competitive prediction performance, meanwhile sustaining lower computational complexity and execution time. Finally, we compare RNN models against deep neural networks along with the well-established support vector regression, clearly highlighting the supremacy of the recurrent ones, as well as discuss future extensions of the introduced work. [ABSTRACT FROM AUTHOR]

Simple Summary: Steel production emits manganese and other heavy metals into the environment, where these byproducts can lead to health problems in humans and other animals. Birds have historically been used to detect harmful chemicals in the air, and in this paper, we show that this role continues into the present day. Feathers of a small terrestrial songbird, the Tufted Titmouse, were collected from live birds captured both near (0.3 km) and far (4.0 km) from steel production facilities in western Pennsylvania, USA. Tail feathers from birds living near the facilities had significantly higher amounts of manganese embedded in their structure compared to the feathers of birds living farther away. This shows that manganese is present in the environment, is localized at industrial sites, is encountered by birds, and is sequestered in their tissues. Animals could suffer ill health if exposures are high enough. Feather data cannot be used to assess the magnitude of this health risk since metals laid down within feathers are no longer circulating in the birds' bodies. However, our data do suggest that the environmental monitoring of manganese should continue, as inadvertent exposure is still occurring. Human industry and land use has led to the anthropogenic release of manganese (Mn) into the air and soil near manufacturing centers. Overexposure to Mn can cause considerable health problems in birds. We studied whether the concentration of Mn in bird feathers correlates with the distance to point sources of Mn air emissions. Feathers were collected from Tufted Titmice (Baeolophus bicolor) at two sites in western Pennsylvania, USA. One site was in proximity (0.3 km) to a steel plant with documented Mn releases, and the other site was in a different town about 4.0 km away from other steel plants with documented Mn releases. Using the microwave plasma–atomic emission spectrometer (MP–AES), we found that tail feathers collected from nearest to a steel plant had a significantly higher concentration of Mn compared to the samples from the site further from the emission source. A body mass index was calculated for each set of birds; however, the indices did not vary significantly. This is the first published study of Mn sequestration in Tufted Titmouse feathers. This study develops our general understanding of the potential use of bird feathers as non-invasive bioindicators of environmental metal exposure. [ABSTRACT FROM AUTHOR]

Advance directives are intended to extend patient autonomy by enabling patients to prospectively direct the care of their future incapacitated selves. There has been much discussion about issues such as whether the future incompetent self is identical to the agent who issues the advance directive or whether advance directives can legitimately secure patient autonomy. However, there is another important question to ask: to what extent and in what conditions is it ethically appropriate for one to limit the liberty or agency of one's future incompetent self by issuing an advance directive? In this paper, I use a virtue‐ethical approach to explore this question, focusing on the case of an advance directive for the future self with moderate dementia. First, I examine virtuous attitudes with regard to autonomy and argue that one can manifest vices or ethically undesirable character traits in trying to intervene in the future self's life. In particular, I argue that this case can manifest vices such as disdainfulness, intellectual arrogance, and self‐dictatorship, which is the vice of trying to control one's life to an excessive degree, and that a self‐dictator fails to give due moral consideration to the future self's liberty or agency. I then introduce the Daoist idea of wu‐wei, which recommends embracement of what happens in one's life, as one of the possible remedies for the overemphasis on the value of autonomy and control. [ABSTRACT FROM AUTHOR]

Work zone safety is a high priority for transportation agencies across the United States. Enforcing speed compliance in work zones is an important factor for reducing the frequency and severity of crashes. This paper uses connected vehicle trajectory data to evaluate the impact of automated work zone speed enforcement on three work zones in Pennsylvania and two work zones in Indiana. Analysis was conducted on more than 300 million datapoints from over 71 billion records between April and August 2021. Speed distribution and speed compliance studies with and without automated enforcement were conducted along every tenth of a mile, and the results found that overall speed compliance inside the work zones increased with the presence of enforcement. In the three Pennsylvania work zones analyzed, the proportions of vehicles travelling within the allowable 11 mph tolerance were 63%, 75% and 84%. In contrast, in Indiana, a state with no automated enforcement, the proportions of vehicles travelling within the same 11 mph tolerance were found to be 25% and 50%. Shorter work zones (less than 3 miles) were associated with better compliance than longer work zones. Spatial analysis also found that speeds rebounded within 1–2 miles after leaving the enforcement location. [ABSTRACT FROM AUTHOR]

The main challenge in addressing constrained multi-objective optimization problems (CMOPs) lies in achieving a balance among convergence, diversity, and feasibility. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm with Pareto estimation via neural network named CMOEA-PeNN. In order to exploit and explore the decision space, the proposed algorithm employs a dual-population mechanism, which is trained with a self-organizing map (SOM). Firstly, the population distribution structure in decision space is mapped to objective space while preserving neighborhood information, and then the neuron weight is utilized to estimate the Pareto front (PF). Secondly, a novel approach is devised to preserve the feasibility of the population and enhance the estimation of the Pareto front by SOM. The achievement scalarizing function (ASF) is employed to choose promising solutions. This strategy could guide the population toward the optimal solution while exploring the small feasible regions. Finally, the performance of CMOEA-PeNN is compared with five state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) on three widely used benchmark problems and a real-world problem. The experimental results show that CMOEA-PeNN could archive competitive performance in solving CMOPs. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to highlight a less illustrated topic in Romanian bibliographic resources. According to the World Health Organization reports, breast cancer is one of the most common types of cancer worldwide. The sequelae of mastectomy are systematised as follows: conditions secondary to the application of local radiotherapy, systemic conditions after oncological treatment and psychosocial conditions. The most common side effect of the treatment applied for breast cancer is lymphoedema and, according to a study carried out in Pennsylvania, 42% of patients develop lymphoedema. Some authors classify it into mild, moderate and severe, but others highlight four stages of lymphoedema development. Physiotherapy has a major role in post-mastectomy recovery and requires various kinetic means, which can be both specific (physical exercise, hydrokinetic therapy, massage, posture) and non-specific (multilayer compression bandage and wearing a compression sleeve). Over time, a lot of clinical studies have been conducted on the implications of physical therapy for post-mastectomy functional recovery. Thus, physical exercise performed during the kinetic programme improves muscle strength, the activity of the cardiorespiratory system and self-esteem. Several authors recommend the practice of aerobic exercise and moderate-intensity exercise twice a week during chemotherapy treatment to prevent the development of lymphoedema. [ABSTRACT FROM AUTHOR]

Background: The aim of this paper is to describe the utility of various recruitment modalities utilized in the Working to Increase Stability through Exercise (WISE) study. WISE is a pragmatic randomized trial that is testing the impact of a 3-year, multicomponent (strength, balance, aerobic) physical activity program led by trained volunteers or delivered via DVD on the rate of serious fall-related injuries among adults 65 and older with a past history of fragility fractures (e.g., vertebral, fall-related). The modified goal was to recruit 1130 participants over 2 years in three regions of Pennsylvania.Methods: The at-risk population was identified primarily using letters mailed to patients of three health systems and those over 65 in each region, as well as using provider alerts in the health record, proactive recruitment phone calls, radio advertisements, and presentations at community meetings.Results: Over 24 months of recruitment, 209,301 recruitment letters were mailed, resulting in 6818 telephone interviews. The two most productive recruitment methods were letters (72% of randomized participants) and the research registries at the University of Pittsburgh (11%). An average of 211 letters were required to be mailed for each participant enrolled. Of those interviewed, 2854 were ineligible, 2,825 declined to enroll and 1139 were enrolled and randomized. Most participants were female (84.4%), under age 75 (64.2%), and 50% took an osteoporosis medication. Not having a prior fragility fracture was the most common reason for not being eligible (87.5%). The most common reason provided for declining enrollment was not feeling healthy enough to participate (12.6%).Conclusions: The WISE study achieved its overall recruitment goal. Bulk mailing was the most productive method for recruiting community-dwelling older adults at risk of serious fall-related injury into this long-term physical activity intervention trial, and electronic registries are important sources and should be considered. [ABSTRACT FROM AUTHOR]

Although water and energy systems are interdependent, they are still designed individually due to their complexity. This paper proposes a multiple input multiple output (MIMO) model predictive control (MPC) for demand management of an integrated water distribution system (WDS) supplied by an islanded microgrid (MG) system, offering an additional opportunity for energy-saving at the WDS. This strategy benefits the island and remote communities, which have limited access to the centralized electric grid. The existing literature either used an open-loop approach for energy management of water-energy microgrids or ignored the interconnected nature and MIMO nature of these systems. Compared to that, the proposed closed-loop energy management system is developed to address the detailed dynamics of assets, the inherent interconnections between water and energy networks, and to provide resilience as well as energy saving and demand management capabilities. The integrated MPC algorithm is designed in discrete time, utilizing a state-space model and sequential quadratic programming (SQP) as the solution method. Considering the intensive use of electricity by the pumps in WDS, their energy consumption is accommodated by an islanded microgrid system that incorporates a diesel generator, renewable energy sources (wind and solar), and a battery bank that serves as an energy source and a storage device. The proposed approach is investigated using base water demand from a real-world community, Telford, in Pennsylvania. Several case studies, such as variations in renewable energy contributions and increments of the control actions, are carried out to demonstrate the feasibility and reliability of the integrated MPC to provide efficient water and energy demand management concurrently. • Model predictive control algorithm of the interconnected water-energy is developed. • The full dynamics of energy-consuming assets in the water system are considered, including quadratic pump relations. • Effect of the renewable energy variations on the 24-hr energy dispatch is studied. • Multiple optimal configurations result in up to $360k/year savings, reduced carbon footprint, and smoother control. • The developed framework is applied in a Telford, PA case study, showcasing its real-world implementation. [ABSTRACT FROM AUTHOR]

We use the synthetic control method to determine the economic impact of shale exploitation on Ohio, Pennsylvania, and West Virginia. Estimation results are mixed. The shale development decreased the poverty rate and increased the employment growth rate in Pennsylvania and West Virginia in the short run. Top oil and gas producing counties in West Virginia also experienced short‐term personal income growth due to fracking. However, most of the positive impacts disappeared a few years after the initial boom periods. The shale development did not bring significant economic benefits to Ohio. Further, shale drilling activities exert a potential long‐term negative effect on population growth in all three states. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

• The problem of historical steel truss bridges preservation is presented. • A novel hybrid deductive failure analysis approach (Analytical Fault Tree - AFT) is developed. • Numerical models investigate fault scenarios including fatigue and concurring causes. • AFT and fault scenarios are applied to a historical bridge located in the East of Spain. • Guidelines and conservation strategies are obtained for the investigated bridge. Historical steel bridges represent an important construction typology integrating the constructive heritage of the past century that needs to be preserved. Exposure to fatigue phenomenon, aging, improper design or execution, extreme events, and other aggressive environmental agents can seriously compromise the conservation and performance of these structures as some recent catastrophic collapses have shown (Mississippi River bridge, Minneapolis, Minnesota 2007; Kinzua Bridge State Park, Pennsylvania 2003). In this context, the diagnostic of historical steel bridges becomes of basic importance to identify and implement maintenance, monitoring and conservation strategies. Fault Trees are useful tools for practitioners that provide a complete overview of possible failures. In the related literature, this instrument is mainly applied with approaches that are based on previous experience or failures detected in similar structures, and so the Fault Tree can only provide qualitative support. This paper proposes a new method to achieve an "Analytical" Bridge Fault Tree linking the technician's experience and the numerical simulations of different fault scenarios. The latter are achieved by a numerical model able to consider fatigue failure and different concurring causes in the analysis (e.g., and aggressive phenomena, corrosion, defects, lack of maintenance). The proposed approach was applied to a historical railway steel truss bridge located in the East of Spain (Valencia Region) in order to show its applicability and potential. [ABSTRACT FROM AUTHOR]

This paper describes a new method for detecting individual tree stems that was designed to perform well in the challenging hardwood-dominated, mixed-species forests common to the northeastern U.S., where canopy height-based methods have proven unreliable. Most prior research in individual tree detection has been performed in homogenous coniferous or conifer-dominated forests with limited hardwood presence. The study area in central Pennsylvania, United States, includes 17+ tree species and contains over 90% hardwoods. Existing methods have shown reduced performance as the proportion of hardwood species increases, due in large part to the crown-focused approaches they have employed. Top-down approaches are not reliable in deciduous stands due to the inherent complexity of the canopy and tree crowns in such stands. This complexity makes it difficult to segment trees and accurately predict tree stem locations based on detected crown segments. The proposed voxel column-based approach has advantages over both traditional canopy height model-based methods and computationally demanding point-based solutions. The method was tested on 1125 reference trees, ≥10 cm diameter at breast height (DBH), and it detected 68% of all reference trees and 87% of medium and large (sawtimber-sized) trees ≥28 cm DBH. Significantly, the commission rate (false predictions) was negligible as most raw false positives were confirmed in follow-up field visits to be either small trees below the threshold for recording or trees that were otherwise missed during the initial ground survey. Minimizing false positives was a priority in tuning the method. Follow-up in-situ evaluation of individual omission and commission instances was facilitated by the high spatial accuracy of predicted tree locations generated by the method. The mean and maximum predicted-to-reference tree distances were 0.59 m and 2.99 m, respectively, with over 80% of matches within <1 m. A new tree-matching method utilizing linear integer programming is presented that enables rigorous, repeatable matching of predicted and reference trees and performance evaluation. Results indicate this new tree detection method has potential to be operationalized for both traditional forest management activities and in providing the more frequent and scalable inventories required by a growing forest carbon offsets industry. [ABSTRACT FROM AUTHOR]

Since the Pennsylvania oil rush of 1859, petroleum has quickly become the dominant fuel of industrial society. The "Peak Oil" debate focused on whether or not there was an impending production crunch of cheap oil, and whilst there have been no shortages across the globe, a shift from conventional to unconventional oil liquids has occurred. One aspect of this shift was not fully explored in previous discussions–although of some importance in a low-carbon energy transition context: the extent to which the net-energy supply of oil products is affected by the use of lower quality energy sources. To fill this gap, this paper incorporates standard EROI (energy-return-on-investment) estimates and dynamic decline functions in the GlobalShift all-liquids bottom-up model on a global scale. We determine the energy necessary for the production of oil liquids (including direct and indirect energy costs) to represent today 15.5% of the energy production of oil liquids, and growing at an exponential rate: by 2050, a proportion equivalent to half of the gross energy output will be engulfed in its own production. Our findings thus question the feasibility of a global and fast low-carbon energy transition. We therefore suggest an urgent return of the peak oil debate, but including net-energy issues and avoiding a narrow focus on 'peak supply' vs 'peak demand'. [Display omitted] • Global gross and net-energy of oil liquids production is determined from 1950 to 2050. • Energy required for production is estimated to be 15.5% of the actual gross energy. • Oil liquids become a limit to a rapid and global low-carbon energy transition. • The peak supply vs. peak demand dispute needs to be re-examined. • Focus should be put instead on net-energy transition and wise energy consumption. [ABSTRACT FROM AUTHOR]

• Probabilistic life-cycle cost analysis for concrete bridge decks under corrosion. • Agency and user cost quantification for decks with different reinforcement types. • Detailed traffic delay estimation based on vehicle queuing and traffic control procedures. • Analyze effect of concrete cover, bridge location, traffic volume, and number of lanes. • Optimum deck reinforcement selection based on bridge attributes and life-cycle cost. Reinforced concrete bridge decks with conventional reinforcement may experience deterioration due to deicing salts or marine environmental conditions. Routine maintenance, rehabilitation, and repair activities are implemented to maintain an acceptable performance level. In addition to the significant financial resources required to conduct these activities, traffic congestions at work zones can lead to an increase in delays and crash risk for road network users, as well as other environmental and social impacts. These user costs arising from traffic congestions can be significantly higher than the cost of maintenance, rehabilitation, and repair activities. Despite the higher initial unit cost, bridge decks constructed with corrosion resistant reinforcement can provide a longer service life and require less maintenance activities. Accordingly, from a life-cycle cost perspective, corrosion resistant reinforcement could be a better alternative compared to conventional reinforcement for bridge decks. This paper presents a probabilistic framework to estimate the life-cycle cost associated with bridge decks constructed with different reinforcement alternatives. The framework accounts for reinforcement deterioration to evaluate the maintenance activity timeline considering influential corrosion-related parameters (e.g., reinforcement type, concrete cover, and bridge location, among others). Key attributes affecting traffic delays, including traffic volume, number of lanes, and vehicular queuing, are also included in the framework. Monte Carlo simulation is used to predict the corrosion-induced cracking time, service life of the bridge deck, and the life-cycle cost under uncertainty. A bridge located in Pennsylvania is used as an illustrative example to demonstrate the application of the proposed framework. The results show that the concrete cover, chloride exposure condition, average daily traffic, and number of traffic lanes have a significant effect on the life-cycle cost of reinforced concrete bridge decks; especially for those constructed with conventional reinforcement. The presented framework can be beneficial in selecting the optimum bridge deck reinforcement type from a life-cycle cost perspective. [ABSTRACT FROM AUTHOR]