Short-term Approach Research Articles

Leadership and Karma: doing good or doing well?

Modern management has much to learn from ancient wisdoms. Management structures based on corporate trends were transferred from business to services such as healthcare to promote cost-efficiency and productivity. In this article, I argue that the short-term approach of corporate leaders being brought into healthcare for 'transformation' has led to a trail of service dismemberment with no discernible clinical gain for those we seek to serve. Bhagwad Gita, the ancient Hindu scripture on right conduct, is an exemplar of how the primary aim of leaders should be to provide better service rather than serve personal interests or those of the 'business' of healthcare.

Who works! from fragmentation to coherence through collaborative, constructive communication

PurposeThe paper seeks to assist public sector leaders to take a balanced and impactful approach to transformation programmes which aim to deliver integrated healthcare. The paper highlights the balance of attention paid in these programmes across elements of leadership, strategy, structure and people, and aims to highlight where this balance can sit to encourage more successful and sustainable transformations, with an increased focus on interpersonal and inter-professional engagement and interaction across workforce and service users.Design/methodology/approachThe project involved a literature review which identified themes that were in turn used to inform an approach to a desk review of journal articles written about past integration programmes in healthcare. A coding framework was developed to assess the articles in the desk review to identify where the focus of attention lay in the approaches to integration.FindingsThere is a spread of activity across all four themes (leadership, strategy, structure and people) in the assessed cases, but the emphasis tends to be towards static, short-term approaches, with a noticeable lack of focus on the aspects required to deliver long-term sustained transformation. There is a need for improved balance between structural and relational approaches to transformation.Research limitations/implicationsThe paper focuses on nine examples of transformation programmes and would benefit from further development and use of the coding framework.Practical implicationsThe framework that emerges from this project can contribute to the development of a proactive model to assist transformation leads and decision makers bring a more balanced, thoughtful and impactful approach to integrating health and care services. In particular, the findings point to an overuse of structural approaches to change and transformation, which could include project management methods, for example, which become the product of the initiatives, rather than enablers of leaders’ visions and people’s interpersonal and interprofessional engagement and interactions.Originality/valueBuilding on existing research, this paper makes a valuable contribution to the discourse on how to deliver the required health and care integration agenda in a more accessible way and sustainably, moving away from short-term, quick-fix approaches and considers how to accommodate the role of interpersonal interaction as the vehicle for change.

Long Short-Term Memory Approach to Predict Battery SOC

Estimating the ‘State of Charge’ (SOC) is a complex endeavour. Data-driven techniques for SOC estimation tend to offer higher prediction accuracy compared to traditional methods. With the progression of Artificial Intelligence (AI), machine learning has found extensive applications across various fields such as infotainment, driver assistance systems, and autonomous vehicles. This paper categorizes the machine learning techniques utilized in Battery Management System (BMS) applications and employs a modern supervised neural network approach to predict SOC. Accurate SOC estimation is crucial to prevent battery failures in critical situations, such as during heavy traffic or when traveling with limited access to charging stations. Long Short-Term Memory (LSTM) networks are particularly adept at classifying, processing, and predicting based on time series data. These models are capable of capturing and retaining features over time, making them suitable for this study. The model's predicted SOC closely matches the true SOC, and the SOC prediction error remains nearly zero even with a large sample of input data.

Meet the neighbor: framing the European Union and African Union perspectives on the Farm to Fork goals

ABSTRACT The debate surrounding the Farm to Fork Strategy and the European Green Deal with the new sustainability standards proposed by the European Commission to achieve a more sustainable food system and climate-neutral continent by 2050 is increasing concerns and questions among different stakeholders along the food chain and third countries that will be impacted by these measures, if adopted. This analysis aims to expand the debate studying the different perspectives on the relationship food security/sustainability of the European Union and the African Union through the framing of the documents produced by these institutions since 2007. The results show differences in the approach about this relationship seen by the European Union as a long-term goal that looks at the protection of the environment and natural resources while the production changes to be more resilient to external shocks and market fluctuations. Meanwhile, the African Union uses a short-term approach due to the urgency felt by the continental unique environment that makes production development and adapting a priority over environmental issues. These differences need to be targeted to avoid current and future divergences that could impact on the application of the aforementioned standards and to ensure food for all without compromising the future of our ecosystems.

Impact of Modified Diet, Swallowing Exercises, and Neuromuscular Electrostimulation on Severity of Oropharyngeal Dysphagia of Geriatric Patients.

Background and Objectives: Oropharyngeal dysphagia is a common swallowing disorder, characterized by difficulties in moving food and liquids from the mouth to the esophagus; it is particularly prevalent among older adults with neurological conditions. This study aimed to evaluate the effectiveness of a short-term complex treatment protocol combining dietary modifications, swallowing exercises, and transcutaneous neuromuscular electrostimulation in reducing the oropharyngeal dysphagia severity and aspiration risk among geriatric patients. Materials and Methods: A total of 64 participants aged 60 and older, with oropharyngeal dysphagia, at LSMU Kaunas Hospital between May 2021 and April 2023, were included in the study after excluding those with significant comorbidities. Diagnostic assessments included the water swallow test and Fiberoptic Endoscopic Evaluation of Swallowing, conducted before and after treatment. Results: The results indicated a statistically significant reduction in the severity of oropharyngeal dysphagia, with 18.8% of patients showing improvements from moderate to mild dysphagia and 33.3% from severe to moderate. Additionally, the median PAS score was four points (IQR 3-6) before treatment and significantly decreased to three points (IQR 2-4) after treatment (p < 0.001). Conclusions: These findings suggest that even a short-term multidisciplinary approach that lasts 10 days can effectively alleviate the symptoms of oropharyngeal dysphagia, enhance patient safety, and improve swallowing among geriatric patients suffering from this condition.

Extrinsic Motivation vs. Intrinsic Motivation: Key Factors Influencing Farmers' Land Quality Protection Behavior in China.

The crowding effect of individual behavior motivation is a focal point in various disciplines, such as economics and social psychology. Understanding the motivation crowding effect in the context of pro-environmental behavior of farmers is crucial for formulating agricultural environmental policies. However, there is limited knowledge about the motivation crowding effect on farmers' land quality protection behavior, especially in developing countries. This study employs stratified regression models, propensity score matching models, seemingly unrelated regression models, and simple slope analysis methods to analyze the impact of extrinsic motivations (external incentives such as increasing farming income and production yield) and intrinsic motivations (internal drives such as personal satisfaction and responsibility) on farmers' land quality protection behavior, as well as the motivation crowding effect between extrinsic and intrinsic motivations. The analysis is based on survey data from 1064 smallholder farmers in five provinces in China: Zhejiang, Jiangsu, Henan, Shaanxi, and Gansu. The results indicate that both extrinsic and intrinsic motivations have a positive impact on farmers' land quality protection behavior. Notably, extrinsic motivation exhibits a crowding-out effect on intrinsic motivation, suggesting a motivation crowding effect. While promoting farmers' land quality protection behavior through economic incentives is a feasible short-term solution, it may not effectively foster the long-term formation of such behaviors. Given the current context of severe land pollution, alleviating this issue through economic incentives represents a short-term policy approach. Thus, transitioning from short-term to long-term solutions by enhancing farmers' intrinsic motivation to promote farmers' land quality protection behavior is essential for the government to consider in future land protection policy formulation.

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.

Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting

Increasing global energy demands and decreasing stocks of fossil fuels have led to a resurgence of research into energy forecasting. Artificial intelligence, explicitly time series forecasting holds great potential to improve predictions of cost and demand with many lucrative applications across several fields. Many factors influence prices on a global scale, from socio-economic factors to distribution, availability, and international policy. Also, various factors need to be considered in order to make an accurate forecast. By analyzing the current literature, a gap for improvements within this domain exists. Therefore, this work suggests and explores the potential of multi-headed long short-term memory models for gasoline price forecasting, since this issue was not tackled with multi-headed models before. Additionally, since the computational requirements for such models are relatively high, work focuses on lightweight approaches that consist of a relatively low number of neurons per layer, trained in a small number of epochs. However, as algorithm performance can be heavily dependent on appropriate hyper-parameter selections, a modified variant of the particle swarm optimization algorithm is also set forth to help in optimizing the model’s architecture and training parameters. A comparative analysis is conducted using energy data collected from multiple public sources between several contemporary optimizers. The outcomes are put through a meticulous statistical validation to ascertain the significance of the findings. The best-constructed models attained a mean square error of just 0.044025 with an R-squared of 0.911797, suggesting potential for real-world use.

Operational Resilience Assessment in DC Shipboard Microgrids: A Short-Term Energy Imbalance Approach Based on Frequency-Domain Metrics

Operational Resilience Assessment in DC Shipboard Microgrids: A Short-Term Energy Imbalance Approach Based on Frequency-Domain Metrics

Interdisciplinary cooperation with solution-focused brief therapy to reduce job stress, burnout, and coping in Chinese nurses: A randomised controlled trial

BackgroundIncreased job stress and burnout are common among clinical nurses in China, reducing their work efficiency and nursing quality. Multiple studies have been conducted on brief interventions to reduce job stress and burnout among nurses. However, research has been hampered by a lack of psychological knowledge among nursing staff. AimTo assess a solution-focused brief therapy for enhancing nurses' coping styles and mitigating job stress and burnout based on interdisciplinary cooperation. DesignThis randomised controlled trial. SettingsWe randomly allocated 60 nurses working at a tertiary general hospital in a city in China to an experimental (n = 30) or a control group (n = 30). MethodsA collaborative research team comprising psychology and nursing specialists was established to conduct this study. The control group received routine psychological intervention, whereas the intervention group received a solution-focused brief therapy based on interdisciplinary cooperation. Chinese nurses’ job stressors, Maslach Burnout Inventory, and simplified coping style questionnaire were used to evaluate both groups before and after the intervention. ResultsA comparison of the scores for job burnout, total job stress, and positive coping before and after the interventions revealed significant improvements in the experimental group, whereas the scores for emotional exhaustion, depersonalisation, and total job stress in the control group demonstrated statistically significant differences. No significant differences were observed in the scores for negative coping, personal accomplishment, and coping style between the test and control groups before and after the intervention. ConclusionsSolution-focused brief therapy implemented through interdisciplinary collaboration effectively enhances clinical nurses' active coping style, alleviates job stress and burnout, and offers valuable guidance for nursing management practices.

지속가능한 프로젝트 관리를 위한 프로젝트 제어 방안: 도로 침수 예방 사례 프로젝트를 중심으로

As environmental pollution, energy depletion, and maintenance difficulties due to changes in social requirements arise during project progress, interest in the sustainability of project management has increased. Accordingly, sustainable project management theories have been proposed to complement the short-term goal-focused approach and performance evaluation methods that existing project management methods have adhered to. However, the sustainable project management theories have not been extended to empirical research. In this paper, we provides a case study of a sustainable project management framework and control mechanisms such as a collaboration model and incentive model using a road flood prevention project as an example. The case project was a project for improving urban infrastructure and was carried out with consideration for environmental, social, and economic sustainability. Through sustainable project management, the case project was able to achieve its sustainability goals and shows the results of the application of the control mechanisms.

A short-term vehicle speed prediction approach considering dynamic traffic scene

Vehicle speed serves as a crucial indicator in traffic flow efficiency and safety evaluation. Previous researches typically utilize the time-series data of target vehicle speed or the relative speed and distance between the target and leading vehicles as speed prediction model input, which are suitable for stable and single-vehicle scenes. However, the surrounding traffic scenes dynamically changes during the vehicle’s driving process, the driver needs to allocate attention to the key scene vehicles in the surrounding traffic scenes and adjust the current speed based on these vehicles’ behavior. Therefore, this paper proposes a short-time vehicle speed prediction model considering the dynamic traffic scenes. A dynamic grid method is adopted to divide the target vehicle’s surrounding areas into left front, front, and right front regions. And key scene vehicles are identified based on each region’s state. Then the relative distance, speed, and acceleration data between the target vehicle and key scene vehicles are selected as model inputs. Vehicle trajectory data from the South-North No. 1 Expressway in Hefei and Ranger optimized Temporal Convolutional Network (TCN) are utilized to train the speed prediction model. Experiment results indicated that under 1 s, 2 s, and 3 s prediction lengths, the model’s average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.430 and 0.271 respectively. Compared to the speed prediction model considering the static traffic scenes, the average RMSE and MAE for the three prediction lengths have been reduced by 49.15 % and 56.02 % respectively.

Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach

The stability of power grids is critical in ensuring the consistent and efficient delivery of electricity. However, traditional predictive models often fall short in addressing grid data's intricate and ever-changing nature, making it challenging to maintain grid reliability. This paper introduces a novel hybrid approach that combines Biometric Pattern Recognition (BPR) with Long Short-Term Memory (LSTM) networks to enhance the prediction of smart grid stability. This approach employs BPR techniques to extract essential features from smart grid data by leveraging the pattern recognition capabilities typically used in biometric systems. These techniques are particularly effective in identifying and isolating the most relevant patterns within the complex datasets generated by smart grids. On the other hand, the LSTM-based model is designed to handle the temporal dependencies and nonlinear patterns characteristic of grid data. LSTMs, known for their proficiency in time-series analysis, are well-suited for capturing the sequential nature of grid data, enabling more accurate predictions over time. Integrating BPR and LSTM in this hybrid model addresses several limitations in existing predictive methods. By combining the strengths of both techniques, the model enhances the accuracy of predictions and improves the overall reliability of grid stability assessments. Extensive experiments were conducted using real-world datasets to validate the effectiveness of the proposed hybrid model. The results demonstrate a significant improvement in prediction accuracy, with the BPR-LSTM model achieving a 98.25% increase in accuracy compared to traditional prediction methods. This improvement underscores the potential of the BPR-LSTM hybrid approach to play a pivotal role in advancing the stability and reliability of smart grid systems.

The Effect of Solution-Focused Group Counseling on Emotional Eating Levels in University Students: A Randomized Controlled Trial.

Emotional eating is a risk factor for obesity. It should be recognized and prevented at an early stage. The randomised control trial study aimed to determine the effect of internet-based solution-focused short-term group counseling on emotional eating levels in nursing students. Sixty students were assigned to the experimental and control groups by simple random sampling method. Those in the experimental group received of solution-focused counseling, and those in the control group received of healthy nutrition training. The data analysed by chi-square test, paired sample t test, groups t-test and repeated groups Anova test. A p-value less than 0.05 was considered statistically significant. A statistically significant difference was found between the pre-test, post-test and follow-up test body mass indexes of those in the experimental group (p < .05). In addition, there was a statistically significant difference between the group's pre- and post-test emotional eating scores (p < .05), and no statistically significant difference was found between their Difficulties in Emotion Regulation Scale scores (p > .05). The short-term solution-focused approach was found to be effective in reducing the emotional eating levels of students.

“I believe the Euro 7 legislation takes a too short-term approach”

“I believe the Euro 7 legislation takes a too short-term approach”

Twists to Classical Conditioning of Adult Drosophila.

Memory has been extensively studied in Drosophila since the early 1970s. Straightforward aversive and appetitive conditioning paradigms train populations of flies to associate the pairing of one of two odors with either punishment or reward. After training, the flies show either preferential avoidance or approach behavior, to the appropriate odor, when given a choice between the two odors in a simple T-maze apparatus. These basic experimental approaches have proven useful in understanding the genetic, molecular, cellular, and neuronal network bases of various valence-specific memories in the fly brain. In addition, numerous modifications to these assays have permitted the study of a broad range of memory-related phenomena. Labile short-term avoidance and approach memories can be readily distinguished from more stable "consolidated" long-term memory equivalents. Prior or subsequent experience of the training cues, and manipulations of the flies' condition, have revealed how parallel competing memories and incompatible states can temporarily interfere with memory retrieval, providing insight into mechanisms of forgetting. Recent studies have also modified the training and testing apparatus to allow simultaneous presentation of odors and colors, providing insight into mechanisms of multisensory learning.

A Small Macro Econometrıc Model of Azerbaıjan Economy

In the presented article, the construction of the Macroeconometric model for short- and medium-term forecasting of indicators of socio-economic development in the economy of Azerbaijan has been carried out. The model consists of 64 variables (41 endogenous, 23 exogenous) and 41 equations, built in four blocks (national income account, deflators, balance of payments and public finance). The database of the model is formed in four parts, covering the years 1995-2017 on an annual basis, including national income account, deflators, balance of payments and public finance. The development of the database was carried out together with the State Statistical Committee of the Republic of Azerbaijan (SSC) in full compliance with UN standards. The methodology used includes time series stationarity tests, ARIMA models, vector error correction models and other econometric methods. As a result, a compact macroeconometric model for the economy of Azerbaijan was established. Based on the model, predictions were processed in the short-term scientific research approach and compared with the actual results.

Assessing Private Equity Involvement in Ophthalmology: Parallels With the Past, Concerns for the Future

PurposeTo discuss the history and current state of private equity in ophthalmology and explore potential implications for ophthalmology practices, physicians, and patients. DesignPerspective. MethodsReview of selected articles in ophthalmology and non-ophthalmology medical literature and commentary based on the authors’ experiences. ResultsInvestment in ophthalmology appeals to private equity firms because of high procedural volumes, fragmented private practices, opportunities for real estate ownership, and an aging population in need of eye care. As a result, acquisition of private practices by large private equity companies has swelled since 2015. Metropolitan areas and practices that possess ambulatory surgical centers or a high proportion of retina surgeons receive the greatest investment. However, little objective data exists regarding the benefits of this trend. Initial research suggests that private equity ownership may negatively impact patient care through greater costs, shorter appointments, and fewer Medicare/Medicaid patients seen. Skeptics of private equity also highlight concerns that include financial risk falling primarily on the acquired practice itself, a short-term business approach that rewards profit maximization, and the erosion of physician autonomy. Furthermore, young ophthalmologists express reservations about working for private equity firms. 78% of trainees indicated they would not consider employment by a private equity owned practice and 81.4% of vitreoretinal fellows voiced concerns about autonomy and job security. Proposed legislation aimed at limiting private equity acquisitions is currently under review at the statewide level, adding additional uncertainty to the future of private equity involvement in ophthalmology. ConclusionAlthough investment by private equity offers potential financial rewards and operational improvements for practices, it also raises significant concerns reminiscent of those experienced by physician practice management companies in the 1990s. Nonanecdotal data is needed to assess the benefits and future of private equity involvement in ophthalmology.

No Country for Sick Men: The Political Determinants of Health Policy in Poland

Context: The objective of this article is to explain the political factors determining the relatively weak performance of the Polish health care (HC) sector. This can be treated as a critical case for several reasons. First, the Poles are among the most unsatisfied patients in the European Union, with one of the lowest life expectancy levels. Second, Poland spends one of the lowest shares of gross domestic product on HC-related expenditures among OECD countries. Third, the country is facing medical personnel shortages. Methods: The analysis is based on the mixed-methods approach. The authors rely on quantitative data outsourced from a survey, which is supplemented by the semistructured, in-depth interviews with selected key HC stakeholders representing patients' advocacy groups, medical personnel organizations, and high-level decision-makers. Findings: The Polish HC system remains weak due to the postcommunist legacy in terms of organization, a short-term approach by politicians, and weak decision-making processes. Conclusions: The HC policy inertia in Poland is determined by a group of interrelated political factors that effectively block the development of any positive reform.

Advanced hybrid empirical mode decomposition, convolutional neural network and long short-term memory neural network approach for predicting grain pile humidity based on meteorological inputs

Grain pile humidity prediction is beneficial to ensure food security, and establishing an effective humidity prediction model is of great significance to the field of grain storage. By taking meteorological and grain temperature data as inputs, we propose a prediction model that combines Empirical Mode Decomposition (EMD), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM). The model was verified in experimental data of three different storage layers of grain piles. The prediction results show that the proposed EMD-CNN-LSTM model has better prediction accuracy than the other three comparison models: CNN-LSTM, CNN and LSTM. From the average results of the entire granary, the MAE, RMSE, and MAPE results are 0.14, 0.18, and 0.25%, respectively, and the MAE value is 44% higher than the previous research method that does not consider meteorological factors. The MAE, RMSE, and MAPE results of the CNN-LSTM method with EMD decomposition were improved by 58%, 53% and 58% respectively compared with the method without EMD decomposition. It can be concluded that taking meteorological factors as model input and integrating EMD methods can improve prediction accuracy. The constructed prediction model shows effective prediction results in different storage layers of grain pile, which provides new insights for ensuring food security and also provides valuable references for multivariate time series prediction in other fields.