Pseudo-unitary symmetry and the Gaussian pseudo-unitary ensemble of random matrices

Employing the currently discussed notion of pseudo-Hermiticity, we define a pseudo-unitary group. Further, we develop a random matrix theory which is invariant under such a group and call this ensemble of pseudo-Hermitian random matrices as the pseudo-unitary ensemble. We obtain exact results for the nearest-neighbour level spacing distribution for (2 X 2) PT-symmetric Hamiltonian matrices which has a novel form, s log (1/s) near zero spacing. This shows a level repulsion in marked distinction with an algebraic form in the Wigner surmise. We believe that this paves way for a description of varied phenomena in two-dimensional statistical mechanics, quantum chromodynamics, and so on.Comment: 9 pages, 2 figures, LaTeX, submitted to the Physical Review Letters on August 20, 200

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

From habits of attrition to modes of inclusion: enhancing the role of private practitioners in routine disease surveillance

Background: Private practitioners are the preferred first point of care in a majority of low and middle-income countries and in this position, best placed for the surveillance of diseases. However their contribution to routine surveillance data is marginal. This systematic review aims to explore evidence with regards to the role, contribution, and involvement of private practitioners in routine disease data notification. We examined the factors that determine the inclusion of, and the participation thereof of private practitioners in disease surveillance activities. Methods: Literature search was conducted using the PubMed, Web of Knowledge, WHOLIS, and WHO-IRIS databases to identify peer reviewed and gray full-text documents in English with no limits for year of publication or study design. Forty manuscripts were reviewed. Results: The current participation of private practitioners in disease surveillance efforts is appalling. The main barriers to their participation are inadequate knowledge leading to unsatisfactory attitudes and misperceptions that influence their practices. Complicated reporting mechanisms with unclear guidelines, along with unsatisfactory attitudes on behalf of the government and surveillance program managers also contribute to the underreporting of cases. Infrastructural barriers especially the availability of computers and skilled human resources are critical to improving private sector participation in routine disease surveillance. Conclusion: The issues identified are similar to those for underreporting within the Integrated infectious Disease Surveillance and Response systems (IDSR) which collects data mainly from public healthcare facilities. We recommend that surveillance program officers should provide periodic training, supportive supervision and offer regular feedback to the practitioners from both public as well as private sectors in order to improve case notification. Governments need to take leadership and foster collaborative partnerships between the public and private sectors and most importantly exercise regulatory authority where needed

Bioactive principles, antibacterial and anticancer properties of Artemisia arborescens L.

Artemisia arborescens is a medicinal and aromatic plant used in traditionally by the people of Saudi Arabia. This research attempts to evaluate the bioactive constituents of the plant using organic solvents, as well as the antibacterial and anticancer properties of plant extracts. The Phytochemical analysis of methanol extract revealed eleven bioactive constituents, identified by comparing their retention periods and GC-MS profiles to account for 52.45 percent of the studied extract. In the meantime, the extract of pet ether had demonstrated the presence of sixteen significant constituents, six of which were distinct sesquiterpene derivatives. In lipophilic plant extract, three higher alkanes made up 12.49% of the total. These higher alkanes were tetratriacontane (6.55%), hentriacontane (4.17%), and octacosane (1.77%). Studies on antimicrobial activity have revealed that both methanolic and petroleum ether extracts had a broad spectrum of activity against specific human pathogens. Both extracts, however, failed to exhibit any anti-Candida albicans activity. Methanolic extract not shown inhibition in the cell growth of MCF-7 cell, but petroleum ether extract had shown significant anti-cancer activity against MCF-7 cell with an IC50 of 13.49 µg/mL. the results obtained show that A. arborescens have a lot of potential for further research into variety of biological functions, against cancer and microbes.

Systematic Development of Short-Term Load Forecasting Models for the Electric Power Utilities:The Case of Pakistan

Load forecasts are fundamental inputs for the reliable and resilient operation of a power system. Globally, researchers endeavor to improve the accuracy of their forecast models. However, lack of studies detailing standardized model development procedures remains a major issue. In this regard, this study advances the knowledge of the systematic development of short-Term load forecast (STLF) models for electric power utilities. The proposed model has been developed by using hourly load (time series) of five years of an electric power utility in Pakistan. Following the investigation of previously developed load forecast models, this study addresses the challenges of STLF by utilizing multiple linear regression, bootstrap aggregated decision trees, and artificial neural networks (ANNs) as mutually competitive forecasting techniques. The study also highlights both rudimentary and advanced elements of data extraction, synthetic weather station development, and the use of elastic nets for feature space development to upscale its reproducibility at global level. Simulations showed the superior forecasting prowess of ANNs over other techniques in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) and R2 score. Furthermore, an empirical approach has been taken to underline the effects of data recency, climatic events, power cuts, human activities, and public holidays on the model's overall performance. Further analysis of the results showed how climatic variations, causing floods and heavy rainfalls, could prove detrimental for a utility's ability to forecast its load demand in future

Nexus between household energy and poverty in poorly documented developing economies—perspectives from Pakistan

The indicators measuring socioeconomic wellbeing, such as the human development index (HDI) and multi‐dimensional poverty indicator (MPI), recognize energy as an important resource for human development. However, energy did not find due weight in determining HDI or MPI, except as a fractional contributor to MPI calculations. This study presents a regression model to establish an energy–poverty nexus in Pakistan, utilizing a real‐world dataset. Defining poverty in terms of per‐capita income (PCI), the proposed model incorporates education‐based parameters along with the energy‐dependent indicators linked to households in Pakistan. The data aggregated at districts level are extracted from the Census 2017 campaign, Pakistan Bureau of Statistics (PBS). Statistical analyses indicate that energy‐based identifiers correlate well with the PCI and augment the education‐only model, capturing 94% variability in PCI vs. 78% for the education‐only model. The study highlights the criticality of relevant data collection and data‐driven planning in Pakistan for creating synergy in energy planning and poverty alleviation programs and provides recommen-dations for considering energy as an important and integral contributory factor in the human development index (HDI)

Grand Challenges in Global Health: Engaging Civil Society Organizations in Biomedical Research in Developing Countries

The authors discuss the different types of civil society organizations, their role in biomedical research, and the advantages and challenges of working with them

Population‐based cohort study of outcomes following cholecystectomy for benign gallbladder diseases

Background The aim was to describe the management of benign gallbladder disease and identify characteristics associated with all‐cause 30‐day readmissions and complications in a prospective population‐based cohort. Methods Data were collected on consecutive patients undergoing cholecystectomy in acute UK and Irish hospitals between 1 March and 1 May 2014. Potential explanatory variables influencing all‐cause 30‐day readmissions and complications were analysed by means of multilevel, multivariable logistic regression modelling using a two‐level hierarchical structure with patients (level 1) nested within hospitals (level 2). Results Data were collected on 8909 patients undergoing cholecystectomy from 167 hospitals. Some 1451 cholecystectomies (16·3 per cent) were performed as an emergency, 4165 (46·8 per cent) as elective operations, and 3293 patients (37·0 per cent) had had at least one previous emergency admission, but had surgery on a delayed basis. The readmission and complication rates at 30 days were 7·1 per cent (633 of 8909) and 10·8 per cent (962 of 8909) respectively. Both readmissions and complications were independently associated with increasing ASA fitness grade, duration of surgery, and increasing numbers of emergency admissions with gallbladder disease before cholecystectomy. No identifiable hospital characteristics were linked to readmissions and complications. Conclusion Readmissions and complications following cholecystectomy are common and associated with patient and disease characteristics