The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status (...) of the power grid sensor network. The LPR-MLP model consists of two parts: local binary pattern, principal component analysis, and ReliefF are used to process image data and meteorological and mechanical data and the multilayer perceptron method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods. (shrink)

In 2022, the Camden Coalition Medical-Legal Partnership began providing civil and criminal legal services to substance use disorder patients at Cooper University Health Care’s Center for Healing. This paper discusses early findings from the program’s first year on the efficacy of the provision of criminal-legal representation, which is uncommon among MLPs and critical for this patient population. The paper concludes with takeaways for other programs providing legal services in an addiction medicine setting.

Medical-legal partnership (MLP) embeds attorneys and paralegals into care delivery to help clinicians address root causes of health inequities. Notwithstanding decades of favorable outcomes, MLP is not as well-known as might be expected. In this essay, the authors explore ways in which strategic alignment of legal services with healthcare services in terms of professionalism, information collection and sharing, and financing might help the MLP movement become a more widespread, sustainable model for holistic care delivery.

Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm have been (...) investigated for the first time in multivariate drought studies. For this purpose, two semi-arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization, during the period of 1988–2017. MSPI was calculated in 5-time windows of the multivariate drought, including MSPI3–6, MSPI6–12, MSPI3–12, MSPI12–24, and MSPI24–48. The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows had better predictions than the smaller ones. Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously. The hybrid MLP-ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP’s accuracy by 28.5%, which is a significant improvement. According to the evaluations, the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas. (shrink)

This article describes a new type of medical-legal partnership (MLP) that targets the health and justice concerns of people enmeshed in the U.S criminal justice system: a partnership between clinicians who care for people with criminal system involvement and public defenders. This partnership offers an opportunity to not only improve patient health outcomes but also to facilitate less punitive court dispositions, such as jointly advocating for community-based rehabilitation and treatment rather than incarceration.

In vertebrates, members of the cysteine‐rich protein (CRP) family are characterized by the presence of two LIM domains linked to short glycine‐rich repeats. These proteins mediate protein–protein interactions and are of fundamental importance for cell differentiation, cytoskeletal remodeling, and transcriptional regulation. To date, a vast amount of information about vertebrate CRPs has become available, including their biological functions, interacting partners, and three‐dimensional structures. Compatible with a molecular adapter role, structural data reveal that the LIM domains within these proteins represent completely (...) independent folded units bridged by flexible linker regions. The physiological roles for individual CRPs was determined by targeted gene disruption analysis and by identification of common and specific binding partners by means of yeast and mammalian two‐hybrid screens. Several CRP‐like LIM domain proteins with close structural and sequence similarity were identified in arthropods, protozoas and plants, supporting the notion that this subset of LIM domain proteins has been highly conserved over the span of evolution thereby emphasizing the importance of their function. BioEssays 25:152–162, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

This article examines five different Medical-Legal Partnerships associated with Yale Law School in New Haven, Connecticut to illustrate how MLP addresses the social determinants of poor health. These MLPs address varied and distinct health and legal needs of unique patient populations, including: 1) children; 2) immigrants; 3) formerly incarcerated individuals; 4) patients with cancer in palliative care; and 5) veterans. The article charts a research agenda to create the evidence base for quality and evaluation metrics, capacity building, sustainability, and best (...) practices; it also focuses specifically on a research agenda that identifies the value of the lawyers in MLP. Such a focus on the “L” has been lacking and is overdue. (shrink)

Across the country, legal and health care professionals who understand that health outcomes are most influenced by social and environmental conditions have improved patient health by adopting the interdisciplinary MLP health care delivery model. However, the MLP field cannot advance population health, let alone long-term health equity, until it addresses the structural determinants of health inequity that are rooted in discrimination, segregation, and other forms of racial and ethnic subordination.

Palliative care and medical-legal partnership are complementary disciplines dedicated to integrating care to treat the whole patient and intervening before a legal or medical issue is at a crisis point. In this paper, we discuss the founding and operations of the Yale Palliative Medical Legal Partnership, give examples of typical cases, explain special considerations in this area of law, and propose areas for further research.

Evapotranspiration represents the water requirement of plants during their growing season, and its accurate measurement at the farm is essential for agricultural water planners and managers. Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration measurement by machine learning techniques and the least available climatic variables. For this (...) purpose, daily meteorological variables were obtained from three ground meteorological stations in rice cultivation regions of northern Iran during 2003–2016. The ETc rates were calculated by seven meteorological variables, the FAO-56 Penman-Monteith equation, and the regional calibrated rice crop coefficient and considered as the reference data. The MLs, including Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Network, and Group Method of Data Handling, were utilized for ETc modeling. Different input combinations were applied, based on the use of minimum effective variables as input. Results showed that the models showed the most accurate performances in the input combination of four climatic variables: sunshine duration, maximum temperature, relative humidity, and wind speed. Investigating the accuracy of models in rice growth phases showed that the least estimation error belonged to the initial growing stage, which increased during the mid-season and late-season growing stages. A comparison of the models showed that the GMDH model performed better against the other competitors. For this model, both the Nash-Sutcliffe coefficient and R2 were greater than 0.98, and the Root Mean Square Error ranged between 0.214 and 0.234 mm per day in all stations. The current approach showed promising results in rice evapotranspiration modeling by only four common meteorological variables and can be reliably applied for indirect measurement of this variable over the rice farms. The studied approach will have research value for rice and other crops in similar/different climatic conditions. (shrink)

Medical-legal partnerships (MLPs) have the potential to address racial health disparities by improving the conditions that constitute the social determinants of health. In order to live up to this potential, these partnerships must intentionally incorporate seven core racial justice principles into their design and implementation. Otherwise, they are likely to replicate the systemic barriers that lead to racialized health disparities.

Medical-Legal Partnerships (MLPs) have been widely acclaimed for promoting health equity and achieving meaningful outcomes. Yet, little to no research has analyzed if this critical work has been done with communities — through meaningful engagement and building power — or if it has been done for communities without their involvement.

Medical-legal partnerships (MLPs) — collaborative endeavors between health care clinicians and lawyers to more effectively address issues impacting health care — have proliferated over the past decade. The goal of this interdisciplinary approach is to improve the health outcomes and quality of life of patients and families, recognizing the many non-medical influences on health care and thus the value of an interdisciplinary team to enhance health. This article examines the unique, interrelated ethical issues that confront the clinical and legal partners (...) involved in MLPs. We contend that the ethical precepts of the clinical and legal professions should be seen as opportunities, not barriers, to further the interdisciplinary nature of MLPs. The commonalities in ethical approaches represent a potential bridge between legal and health care advocacy for patient/client well-being. Bioethics has a role to play in building and analyzing this bridge: bioethics may serve as a discourse and method to enhance collaboration by highlighting common ethical foundations and refocusing legal and clinical partners on their similar goals of service for patients/clients. This article explores this bridging role of bioethics, through a series of case studies. It concludes with recommendations to strengthen the collaborations. (shrink)

While Medical-Legal Partnerships (MLPs) have improved the health and well-being of the people they serve, most healthcare institutions will only invest in an MLP if they are convinced that doing so will improve its balance sheet. This article offers a detailed estimation of the cost savings that an MLP targeted toward the most acute legal needs would accrue to an academic medical center (AMC) in North Carolina.

Medical-legal partnerships (MLPs) integrate knowledge and practices from law and health care in pursuit of health equity. However, the MLP movement has not reached its full potential to address racial health inequities, in part because its original framing was not explicitly race conscious.

This paper proposes several recurrent neural network-based models for recognizing requisite and effectuation parts in Legal Texts. Firstly, we propose a modification of BiLSTM-CRF model that allows the use of external features to improve the performance of deep learning models in case large annotated corpora are not available. However, this model can only recognize RE parts which are not overlapped. Secondly, we propose two approaches for recognizing overlapping RE parts including the cascading approach which uses the sequence of BiLSTM-CRF models (...) and the unified model approach with the multilayer BiLSTM-CRF model and the multilayer BiLSTM-MLP-CRF model. Experimental results on two Japan law RRE datasets demonstrated advantages of our proposed models. For the Japanese National Pension Law dataset, our approaches obtained an \ score of 93.27% and exhibited a significant improvement compared to previous approaches. For the Japan Civil Code RRE dataset which is written in English, our approaches produced an \ score of 78.24% in recognizing RE parts that exhibited a significant improvement over strong baselines. In addition, using external features and in-domain pre-trained word embeddings also improved the performance of RRE systems. (shrink)

It is shown that a formula of modal propositional logic has precisely the same models as a sentence of the first-order language of a single dyadic predicate iff its class of models is closed under ultraproducts. as a corollary, any modal formula definable by a set of first-order conditions is always definable by a single such condition. these results are then used to show that the formula (lmp 'validates' mlp) is not first-order definable.

The upstream framework presented in public health and medicine considers health problems from a preventive perspective, seeking to understand and address the root causes of poor health. Medical-legal partnerships (MLPs) have demonstrated the value of this upstream framework in the practice of law and engage in upstream lawyering by utilizing systemic advocacy to address root causes of injustices and health inequities. This article explores upstreaming and its use by MLPs in reframing legal practice.

The COVID-19 pandemic laid bare systemic inequities shaped by social determinants of health (SDoH). Public health agencies, legislators, health systems, and community organizations took notice, and there is currently unprecedented interest in identifying and implementing programs to address SDoH. This special issue focuses on the role of medical-legal partnerships (MLPs) in addressing SDoH and racial and social inequities, as well as the need to support these efforts with evidence-based research, data, and meaningful partnerships and funding.

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data (...) utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. (shrink)

Medical-legal partnerships — collaborative endeavors between health care clinicians and lawyers to more effectively address issues impacting health care — have proliferated over the past decade. The goal of this interdisciplinary approach is to improve the health outcomes and quality of life of patients and families, recognizing the many non-medical influences on health care and thus the value of an interdisciplinary team to enhance health. There are currently over 180 MLPs at over 200 hospitals and health centers in the United (...) States, with increasing federal interest and potential legislative support of this model.This article examines the unique, interrelated, and often similar ethical issues that confront the clinical and legal partners involved in MLPs. We contend that the ethical precepts of the clinical and legal professions should be seen as opportunities, not barriers, to further the interdisciplinary nature of MLPs. (shrink)

As reproductive freedoms in the U.S. undergo significant rollbacks, vital reproductive health services — and the care teams delivering them — face escalating legal threats and complexity. This qualitative case-control community-based participatory research study describes how legal problem-solving supports for reproductive care teams serving mothers with opioid use disorder are protective for both patients and care team members. We describe how medical legal partnerships (MLPs) can promote Reproductive Justice and argue for wider adoption of care-team facing legal supports.

Sustainable Public Food Procurement (SPFP) is gaining recognition for its potential to improve the sustainability of food systems and promote healthier diets. However, SPFP faces various challenges, including coordination issues, actor dynamics, infrastructure limitations, unsustainable habits, and institutional resistance, among others. Drawing upon insights from the Multi-Level Perspective (MLP) on socio-technical transitions and the X-curve model on transition dynamics, this study investigates the role of transition intermediaries in facilitating SPFP-induced transformations in food systems. Focusing on four case studies in Spain, (...) we identify common barriers encountered in SPFP and analyse how distinct types of transition intermediaries contribute individually and collectively to address these challenges. Additionally, we explore how intermediary networks evolve throughout different phases of the transition process. Our findings reveal that SPFP barriers are systemic and interconnected, emphasizing the necessity of collective intermediation to overcome these obstacles. Furthermore, our results reveal how collective intermediation is orchestrated by pivotal intermediaries who mobilize diverse transition intermediaries, shaping multiple transition pathways. These intermediaries operate at both food system regimes and niches, challenging the conventional notion that transformative change can only originate from niche efforts. Lastly, we highlight the dynamic and flexible nature of intermediation in SPFP transitions, underscoring the importance of adaptability in strategies as these transitions evolve over time. Practical implications include the need for context-specific, adaptive approaches and strategies that leverage intermediary diversity. This research offers insights for policymakers, practitioners, and scholars into SPFP and broader transitions towards food systems transformation, fostering a more comprehensive understanding of these transition processes. (shrink)

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging ), boosting and extreme gradient boosting regression ), and stacking are employed as ensemble models. Different machine learning approaches, including support vector regression, extreme learning machine, and multilayer perceptron neural network, are adopted as reference models. In order to maximize the determination coefficient value and reduce the root mean square error, hyperparameters are set using (...) the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 and STACK2 models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead. (shrink)

Health systems are integrating medical-legal partnerships (MLPs) into clinical care and increasingly center “complex care” patients. These patients have intersecting medical and social needs and often face systemic inequities that exacerbate their chronic health conditions. This paper describes a role for MLPs in hospital quality initiatives; examines the ethics of MLPs assisting with guardianship and institutionalization of hospital patients including marginalized groups; and advocates for MLP interventions designed to address intersectional and ethical concerns.

This article confronts the challenges and opportunities presented by medical-legal partnerships (MLPs) representing families impacted by the family regulation system. Based on the authors’ experience developing a collaboration between a medical-legal partnership, interdisciplinary law school clinic and nurse home visiting program focused on clients impacted by the family regulation system, the article challenges traditional conceptions of the MLP model and proposes an expanded vision for MLPs to address systemic injustice and improve outcomes for families.

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron, recurrent neural network, and autoregressive integrated moving average on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock (...) market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting. (shrink)

A large body of experience and expertise on the implementation of sustainable public school food procurement policies has developed in recent years. However, there has been little investigation of the values and motivations of the public officials implementing the policies. To address this gap, we examine how the city of Avignon took a step toward transition to local fresh food procurement for public schools, under French government calls for sustainable food products in public canteens. Our analysis combines the Multi-Level Perspective (...) on sustainable transitions with the Public Service Motivation construct. Unlike other studies addressing the MLP at a macro level, we focus on individual motivations behind public action. We demonstrate that staff motivations have a major impact on how policies supporting transition to local fresh food procurement for public schools are implemented. These public officials’ approach to food shows concern for the public interest regardless of financial return. It is not the rational choice that primarily drives the implementation of urban food policies, but normative conformity and affective bonding. Interestingly, we conclude that the success of Avignon’s politically-driven school food procurement policy is essentially due to the catering and procurement staff who overcame the barriers to its implementation. (shrink)

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term (...) Memory are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations. (shrink)

Caring for unrepresented patients encompasses legal, ethical, and moral challenges regarding decision-making, consent, the patient’s values, wishes, best interest, and the healthcare team’s professional integrity and autonomy. In this article, I consider the impact of the aging population and the effects of the social determinants of health and suggest that without preventive intervention, the number of unrepresented patients will continue to increase. The health, social, and legal risk factors for becoming unrepresented require a multidisciplinary response. Medical-Legal Partnerships (MLPs) bring healthcare (...) and legal professionals together to address risk factors and health-harming legal needs. The article discusses the role of MLPs in identifying at-risk individuals, providing preventive interventions, and providing support. I make recommendations and conclude that proactive MLPs offer a sustainable approach to the ethical challenges in caring for unrepresented patients by providing interventions to prevent individuals from becoming unrepresented. (shrink)

The social ecological model (SEM) is a conceptual framework that recognizes individuals function within multiple interactive systems and contextual environments that influence their health. Medical Legal Partnerships (MLPs) address the social determinants of health through partnerships between health providers and civil legal services. This paper explores how the conceptual framework of SEM can be applied to the MLP model, which also uses a multidimensional approach to address an individual’s social determinants of health.

Over the past two decades, we have seen an increase in the use of medical-legal partnerships in health-care and/or legal settings to address health disparities affecting vulnerable populations. MLPs increase medical teams' capacity to address social and environmental threats to patients' health, such as unsafe housing conditions, through partnership with legal professionals. Following the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines, we systematically reviewed observational studies published from January 1993-January 2016 to investigate the capacity of MLPs to address (...) legal and health disparities. We identified 13 articles for qualitative analysis from an initial pool of 355 records. The resulting pool of 13 articles revealed more information regarding the capacity of MLPs to address legal outcomes than their capacity to address health outcomes; only 4 studies directly addressed the impact of MLP intervention on patient wellbeing and/or patient utilization of healthcare services. We call for further evaluation/longitudinal studies that specifically address MLPs' short and long term effects upon patient health disparities. Finally, given the demonstrated capacity of MLPs to address unmet legal needs, and their evident potential in regards to improving health outcomes, we present the MLP model as a framework to address HIV-related legal and health disparities. (shrink)

Unmet legal needs contribute to housing, income, and food insecurity, along with other conditions that harm health and drive health inequity. Addressing health injustice requires new tools for the next generations of lawyers, doctors, and other healthcare professionals. An interprofessional group of co-authors argue that law and medical schools and other university partners should develop and cultivate Academic Medical-Legal Partnerships (A-MLPs), which are uniquely positioned to leverage service, education, and research resources, to advance health justice.

Despite evidence that social factors can result in poor health outcomes, and the emergence of payment models that encourage the use non-medical interventions to improve health, many health care providers do not identify the social determinants of health within patient populations through routine screening. This Article explores the possible reasons for this inconsistency by considering screening practices in medical-legal partnerships, the health care approach most concerned with identifying and treating the social determinants of health. Through an analysis of the results (...) of a national survey and qualitative interviews with MLPs, we discovered that screening is not operationalized or consistent within many MLPs. We conclude that although health care providers may recognize the value of screening, they are not yet embracing the practice, perhaps because of an unspoken fear that fulsome screening identifies so many unmet social and legal needs that community-based resources cannot satisfy demand. This fear is unfounded. Approaches such as MLP demonstrate that social and legal needs can be efficiently treated through collaboration with other professionals, often within the health care setting. Nevertheless, providers must first operationalize screening to truly understand the scope of the need in their patient populations and collaborate to address those needs. (shrink)

One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as (...) a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains. (shrink)