Status Data Research Articles

Advancing Diversity in Microbiology: A 55-Year Retrospective Analysis.

Background For over 50 years, affirmative action helped advance equity, diversity and inclusion (EDI) in educational institutions in the United States (U.S.). However, the recent U.S. Supreme Court decision to end affirmative action in college admissions threatens the progress toward EDI. Objective This study aimed to assess the progress in promoting gender and racial diversity within the discipline of microbiology over a 55-year period. We sought to analyze the representation of women and minority groups in faculty ranks, tenure positions, and leadership to identify disparities and trends and determine who will likely be impacted most with the end of affirmative action. Materials and methods This longitudinal retrospective study utilized publicly available and non-identifiable Association of American Medical Colleges (AAMC) data on full-time microbiology faculty from 1967 to 2021. Faculty members were categorized based on academic ranks and tenure status, while gender and racial data were also considered. Results The analysis revealed a consistent dominance of white faculty, with over 60% representation across all academic ranks throughout the study period. The Asian and female faculty representation decreased in senior academic ranks. We observed a positive trend in the annual increase of women in faculty positions, academic ranks, chairs, and tenure positions. Furthermore, Asian faculty demonstrated the most robust surge in representation. However, disparities persisted for black, Hispanic, and Native American faculty members, reflecting broader challenges in their representation. Discussion Although efforts to enhance diversity within microbiology have yielded positive results, underrepresented minority groups still face obstacles in attaining leadership positions and senior academic ranks. The diminishing proportion of women at higher academic ranks raises concerns about potential attrition or lack of promotion opportunities. The end of affirmative action poses a risk of perpetuating this trend, leading to a decline in diversity among microbiology faculty.

Risk factors for postoperative delirium in orthopaedic hip surgery patients: a database review

BackgroundPostoperative delirium is a common problem affecting admitted patients that decreases patient satisfaction and increases the cost and complexity of care. The purpose of this study was to use the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to compare rates and risk factors of postoperative delirium for total hip arthroplasty (THA) and hemiarthroplasty patients indicated for osteoarthritis or proximal femur fracture.MethodsThe 2021 NSQIP database was queried for patients using Current Procedural Terminology (CPT) codes for THA and hemiarthroplasty and ICD-10 codes for osteoarthritis or proximal femur fracture. Demographic, past medical history, preoperative labs, and functional status data were recorded. Procedural data were also collected. Finally, postoperative outcomes and complications were reviewed.ResultsOverall, 16% of patients had postoperative delirium. Delirium patients were older on average (82.4 years vs. 80.7 years, p < 0.001), had a lower BMI (19.5 vs. 24.8, p < 0.001), were more likely to have a history of dementia (54.6% vs. 13.6%, p < 0.001), were less likely to have an independent functional status (p < 0.001) or live alone (p < 0.001), and were more likely to have sustained a recent fall (p < 0.001). Delirium patients were more likely to be hyponatremic or hypernatremic (p = 0.002), anemic (p < 0.001), and severely dehydrated (p < 0.001), among other lab abnormalities. Delirium patients were also more likely to experience additional postoperative complications, including pneumonia, pulmonary embolism, urinary tract infection, stroke, cardiac arrest, sepsis, and unplanned reoperation and readmission after discharge (all p < 0.05).ConclusionsIn this study, factors associated with postoperative delirium in patients undergoing hemiarthroplasty and THA were identified, including older age, lower BMI, certain medical conditions, decreased functional status, certain lab abnormalities, and postoperative complications. These findings can be used by clinicians to better inform care and to determine when orthopaedic joint replacement patients may be at an increased risk for postoperative delirium.

Diagnostic Model for Alzheimer's Disease Based on PANoptosis-Related Genes.

The pathophysiology of Alzheimer's disease (AD) involves the interplay of three different processes: pyroptosis, apoptosis, and necroptosis. To explore role of PANoptosis, a novel pro-inflammatory programmed cell death pathway, in AD patients. We performed a consensus clustering analysis to identify distinct transcriptional profiles in the samples using the R package "ConsensusClusterPlus". The PANoptosis key genes were obtained by crossing the WGCNA brown module and differentially expressed PANoptosis genes. We accomplished regression analyses using the LASSO-Cox method, combined with pathological status and gene expression data. At the same time, we also constructed PANscore system. The expression of PANoptosis hub genes were validated by qRT-PCR in AD transgenic mice. Our study utilized tissue expression profile data from AD patients to construct three distinct PANoptosis patterns, each with unique molecular and clinical characteristics. We have created a risk scoring system called PANscore, which can analyze patterns specific for each AD patient. Additionally, we observed significantly lower levels of follicular helper T (Tfh) cells in the high PANscore and AD patients. Further analysis revealed a significant negative correlation of Tfh with GSDMD and MLKL. These findings provide a roadmap for personalized patient stratification, enabling clinicians to develop personalized treatment plans for AD patients and advance the field of precision medicine.

Genomic and clinical characteristics of campylobacteriosis in Australia.

Campylobacter spp. are a common cause of bacterial gastroenteritis in Australia, primarily acquired from contaminated meat. We investigated the relationship between genomic virulence characteristics and the severity of campylobacteriosis, hospitalisation, and other host factors.We recruited 571 campylobacteriosis cases from three Australian states and territories (2018-2019). We collected demographic, health status, risk factors, and self-reported disease data. We whole genome sequenced 422 C. jejuni and 84 C. coli case isolates along with 616 retail meat isolates. We classified case illness severity using a modified Vesikari scoring system, performed phylogenomic analysis, and explored risk factors for hospitalisation and illness severity.On average, cases experienced a 7.5 day diarrhoeal illness with additional symptoms including stomach cramps (87.1 %), fever (75.6 %), and nausea (72.0 %). Cases aged ≥75 years had milder symptoms, lower Vesikari scores, and higher odds of hospitalisation compared to younger cases. Chronic gastrointestinal illnesses also increased odds of hospitalisation. We observed significant diversity among isolates, with 65 C. jejuni and 21 C. coli sequence types. Antimicrobial resistance genes were detected in 20.4 % of isolates, but multidrug resistance was rare (0.04 %). Key virulence genes such as cdtABC (C. jejuni) and cadF were prevalent (>90 % presence) but did not correlate with disease severity or hospitalisation. However, certain genes (e.g. fliK, Cj1136, and Cj1138) appeared to distinguish human C. jejuni cases from food source isolates.Campylobacteriosis generally presents similarly across cases, though some are more severe. Genotypic virulence factors identified in the literature to-date do not predict disease severity but may differentiate human C. jejuni cases from food source isolates. Host factors like age and comorbidities have a greater influence on health outcomes than virulence factors.

A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture

Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating scenarios. This paper proposes a deep learning-based energy optimization method for microgrid energy management in the new power system scenarios. This article constructs a microgrid cloud edge collaboration architecture, which collects interactive network status data through terminal devices and network edge sides. A microgrid energy management model is constructed based on Bi-LSTM attention in the network cloud. And the model is sunk to provide real-time and efficient comprehensive load and power generation prediction output optimal scheduling decisions at the edge of the network, achieving collaborative control of microgrid light load storage. The simulation based on the actual available microgrid data shows that the proposed Bi-LSTM attention energy management model can achieve rapid analysis and optimize decision-making within 7.3 seconds for complex microgrid operation scenarios.

Salsal: blockchain for vetting cultural object collections

Many modern cultural object collections suffer from the problem of being obtained in unethical and illegal circumstances. Additionally, information about collections, including their status, object descriptions, and other data need up-to-date information presented to users. We propose a novel blockchain tool called Salsal that enables the vetting of objects, individually or as part of more extensive collections, to meet required ethical and legal guidelines while informing users about relevant information regarding collections. Blockchain provides a better and more rapid way for users to know about collections using a decentralized and immutable ledger technology. Blockchain can be used to incentivize or even pressure collections to vet their objects for ethical and legal guidelines that can benefit the public who use object collections. The prototype software we have made is presented and compared to other blockchains, with code and demonstration provided. We present how our blockchain can enable benefit, providing a useful vetting process for cultural objects, and allowing a user community to contribute to collections in a transparent and secure manner.

Progress and practice of objective measurement of physical behaviors in large-scale cohort research

Due to the limited reliability of traditional self-completed questionnaire, the accuracy of measurement of physical behaviors (physical activity, sedentary behavior and sleep) is not high. With the development of technology, wearable devices (e.g. accelerometer) can be used for more accurate measurement of physical behaviors and have great application potential in large-scale research. However, the data of objective measurement of physical behaviors from large-scale cohort research in Asian populations is still limited. Between August 2020 and December 2021, the 3rd resurvey of China Kadoorie Biobank (CKB) project used Axivity AX3 wrist triaxial accelerometer to collect the data of participants' daily activity and sleep status. A total of 20 370 participants from 10 study areas were included in the study, in whom 65.2% were women, and the age was (65.4±9.1) years. The participants' physical activity level varied greatly in different study areas. The objective measurement of participants' physical behaviors in CKB project has provided valuable resources for the description of 24-hour patterns of physical behaviors and evaluation of the health effect of physical activity, sedentary behavior and sleep as well as their association with diseases in the elderly in China.

How do survivors after out-of-hospital cardiac arrest perceive their health compared to the norm population? A nationwide registry study from Norway

IntroductionSelf-perceived health status data is usually collected using patient-reported outcome measures. Information from the patients’ perspective is one of the important components in planning person-centred care. The study aimed to compare EQ-5D-5L in survivors after out-of-hospital cardiac arrest (OHCA) with data for Norwegian population controls. Secondary aim included comparing characteristics of respondents and non-respondents from the OHCA population. MethodsIn this cross-sectional survey, 714 OHCA survivors received an electronic EQ-5D-5L questionnaire 3–6 months following OHCA. EQ-5D-5L assesses for five dimensions of health (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) with five-point descriptive scales and overall health on a visual analogue scale from 0 (worst) to 100 (best) (EQ VAS). Results are used to calculate the EQ index ranging from −0.59 (worst) to 1 (best). Patient responses were matched for age and sex with existing data from controls, collected through a postal survey (response rate 26%), and compared with Chi-square tests or t-tests as appropriate. ResultsOf 784 OHCA survivors, 714 received the EQ-5D-5L, and 445 (62%) responded. Respondents had higher rates of shockable first rhythm and better cerebral performance category scores than the non-respondents. OHCA survivors reported poorer health compared to controls as assessed by EQ-5D-5L dimensions, the EQ index (0.76 ± 0.24 vs 0.82 ± 0.18), and EQ VAS (69 ± 21 vs 79 ± 17), except for the pain/discomfort dimension. ConclusionsNorwegian OHCA survivors reported poorer health than the general population as assessed by the EQ-5D-5L. PROMs use in this population can be used to inform follow-up and health care delivery.

Food Allergy Reaction Severity and Management in a Diverse Population

Definitive treatment for food allergy reactions including anaphylaxis varies widely by reaction severity and socioeconomic status, but little data exist to characterize the relationship between severity, management, and race and ethnicity. To analyze the differences in reaction severity, epinephrine use, and emergency room (ER) use by race and ethnicity in a large, diverse, food-allergic cohort. We analyzed intake data from participants in the Food Allergy Outcomes Related to White and African-American Racial Differences cohort on the history of food allergy reactions, severity of the reactions, and management associated with each reaction. We used descriptive statistics as well as mixed-effects logistic and Poisson models to describe the differences in reaction severity, ER visits, and total lifetime epinephrine use by race and ethnicity. A total of 784 children were included in the analysis: 425 (54.2%) were non-Hispanic White, 282 (36.0%) were non-Hispanic Black, and 77 (9.8%) were Hispanic/Latino. Non-Hispanic Black children had increased odds of more severe reactions (odds ratio, 1.7; 95% CI, 1.2-2.3) and higher odds of going to the ER (odds ratio, 2.8; 95% CI, 1.4-5.4). Both non-Hispanic Black (incidence rate ratio, 0.4; 95% CI, 0.3-0.5) and Hispanic/Latino (incidence rate ratio, 0.3; 95% CI, 0.2-0.5) children had lower rates of total lifetime epinephrine use. There are significant disparities in the severity and treatment of food allergy reactions by race and ethnicity, resulting in increased ER use and decreased total lifetime epinephrine use. Equipping parents with resources and tools on management of food allergy reactions may result in decreased disparity in access to definitive care.

Preoperative Immunonutrition and Postoperative Outcomes in Radical Pancreaticoduodenectomy Patients.

This study aimed to compare the patient groups who received and did not receive immunonutrition in terms of mortality and morbidity in patients who underwent radical pancreaticoduodenectomy. Two groups were formed from 40 patients who underwent radical pancreaticoduodenectomy in our clinic in 2021. The patients in study group were given enteral immunonutrition support for 5 days preoperatively. For this purpose, a standard enteral immunonutrition product containing arginine, omega-3 fatty acids, and RNA (dietary nucleotides) was used. Patients' data of demographical, laboratory, postoperative complications, and current clinical status were analyzed. Mortality developed in 5 (25 %) patients in the treatment group and 4 (20 %) patients in the control group in the following months (P > .05). The estimated survival rate in the treatment group was 21.8 ± 2.8 months in the treatment group 19.1 ± 1.7 months in the control group (P > .05). The length of hospital stay was 12.89 ± 3.3 days in the treatment group, while it was 16.47 ± 6.83 days in the control group (P < .05). In the postoperative follow-ups, delayed gastric emptying symptoms developed in 3 patients in the treatment group, while the same complication was observed in 9 patients in the control group (P < .05). Surgical site infections occurred in 4 patients in the treatment group and 9 patients in the control group (P < .05). It was observed that preoperative oral immunonutrition before pancreaticoduodenectomy was effective in reducing the risk of delayed gastric emptying after surgery and the length of hospital stay.

PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network.

The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome's potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance. To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications. PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN.

Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes

Energy storage devices and systems with better performance, higher reliability, longer life, and wiser management strategies are needed for daily technology advancement. Among these devices, the supercapacitor is the most preferable due to its high-limit capacitance that esteems more than different capacitors. Today, it is considered a significant challenge to design high-performance materials for supercapacitors by exploring the interaction between characteristics and structural features of materials. Because of this, it is essential to predict capacitance when assessing a material’s potential for use in constructing supercapacitor-electrode applications. Machine learning (ML) can significantly speed up computation, capture complex mechanisms to enhance the accuracy of the prediction and make the best choices based on detailed status data. We aimed to develop a new strategy for the assisted design of high-performance supercapacitor materials by applying ML to analyze the relationship between capacitance and structural features of porous carbon materials (PCMs) using hundreds of experimental data in the literature. In the present study, Linear Regression (LR), Regression Tree (RT), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate the supercapacitor’s capacitance. The effectiveness of the ML models was evaluated in terms of the root mean square error (RMSE), mean absolute error (MAE), and the correlation between expected yield and system-provided yield. The developed ANFIS model, with RMSE, MAE, and R values of 22.8, 39.7647, and 0.90004, respectively, compares favourably regarding prediction performance to other models built for this purpose.

Sensor Network Solutions for Aircraft Route Scheduling and Parking Allocation with Localization and Synchronization

The Wireless Sensor Network (WSN) is the modernized version of the sensor networking, earlier the concerned networking system used to be wired. The wireless and modified features have been able to increase the efficiencies of the networking in routing scheduling of aircraft and allocating the parking. This new approach to the WSN system is not much different from the typical sensor networks framework that contains sensors, a communication system, and a controller. Instead of a communication system, a wireless protocol is applied within the sensor network. The smart system of parking has effectively implied due to its hugely innovative as well as in-ground sensors. This can monitor individual spaces of parking and able to relay the status of occupancy to the smart-sport gateways. Then, this can send the live status data to the platform of the smart cloud. This entire process enables the real-time parking data to be accessed and observed on multiple devices.

Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation

We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Our data are a random 10% sample (553,675 observations) from the Finnish population in 2017. Using annual healthcare cost in 2017 as a response variable, we compare the performance of Random forest, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) to linear regression. As machine learning methods are often seen as unsuitable in risk adjustment applications because of their relative opaqueness, we also introduce visualizations from the machine learning literature to help interpret the contribution of individual variables to the prediction. Our results show that ensemble machine learning methods can improve predictive performance, with all of them significantly outperforming linear regression, and that a certain level of interpretation can be provided for them. We also find individual-level socioeconomic variables to improve prediction accuracy and that their effect is larger for machine learning methods. However, we find that the predictions used for funding allocations are sensitive to model selection, highlighting the need for comprehensive robustness testing when estimating risk adjustment models used in applications.

Combination of several predictors for forensic age estimation

Abstract Background Nearly all practical forensic age diagnostics studies estimate the probability distribution of the age conditional on the developmental status of a certain anatomical feature. Given such a probability distribution, the probability of a person exceeding a certain legal age threshold is computed. In court, forensic experts are often asked to summarize the probabilities obtained by evaluating different age indicators of the same person. Objective The present study demonstrates computation of the age probability distribution conditional on the conjunction of several different age indicators given the age probability distributions conditional on the development status of the respective single anatomical features. Material and methods Data from two distinctively different studies on age estimation were used to join their probability information via Bayes’ theorem. Each of the cited studies is based on the development status of only one of two different anatomical structures: third molar and clavicular epiphysis. Results We derive general formulae for Bayesian information joining in forensic age estimation. Posterior distributions of age class, given the simultaneous statuses of the two anatomical features are generated. Finally, the study presents the technique on an artificial case example. Conclusion Bayes’ theorem can be used in forensic age estimations to combine information from several different anatomical features to yield more precise probability values of age given development status data of several distinctly different anatomical features. Conditional stochastic independence of the single age indicators as used in our article has to be scrutinized and is not generally recommendable.

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions

Background and objectiveDespite the constant improvement of coronary heart disease (CHD) diagnostics and treatment methods it remains one of the main causes of death in most countries around the world. And myocardial infarction with ST segment elevation on the electrocardiogram (STEMI) still is one of the most dangerous clinical variants of CHD. This study aims to develop an explainable machine learning model for in-hospital mortality (IHM) risk prediction in STEMI patients after myocardial revascularization by percutaneous coronary intervention (PCI). MethodsA single-center observational retrospective study was conducted, enrolling 4677 electronic medical records of patients with STEMI after PCI, which were analyzed using statistical analysis and machine learning methods. A pool of potential IHM predictors was identified, and prognostic models were developed and validated based on multivariate logistic regression, random forest, and stochastic gradient boosting methods at two stages of hospital treatment: during the initial physicians examination in the emergency department and immediately after PCI surgery. To explain the IHM prognosis, threshold values of IHM risk factors were determined using 3 grid search methods for optimal cut-off points, calculating centroids and SHapley Additive exPlanations (SHAP). ResultsIHM prognostic models were developed using clinical and functional status data of STEMI patients during two stages of hospital treatment. The IHM prediction accuracy according to the first scenario was AUC = 0.85, and according to the second - AUC = 0.9. Predictors identified and validated in the models were converted into risk factors. Models whose parameters were risk factors demonstrated high forecast accuracy (AUC = 0.87), with the best model formed using the SHAP method. ConclusionsFor the forecast result interpretation risk factors obtained by categorizing continuous variables can be used by assessing the impact of the latter on the end point using the SHAP method.

Study on the Role of Public Sector Human Resource Management in Educational Management Reforms

Abstract The principles and methods of human resource management have been introduced into the field of education for some time, but the analysis of the current state of research, specifically on educational management, is still insufficient. The first part of this paper analyzes the measures that are related to human resource management in education management reform in each sector and further investigates the factor analysis method in the public sector human resource management system. By constructing the factor analysis model and factor calculation method, we can provide an algorithmic basis for studying the role of public sector human resource management in education management reform. The article utilizes the factor analysis method to conduct an empirical study by using the educational resources of different schools in 11 cities of Province A as test objects. According to the study, the first principal component has seven-factor loadings that are higher than 0.72, which indicates that all seven factors have a positive impact on the allocation efficiency of higher vocational education. The second principal component has four-factor loadings greater than 0.80, suggesting that all four factors have a positive effect on the allocation efficiency output of higher vocational education. From the sample data of the school education resource allocation status of each city, it can be obtained that the school education resource allocation status of city A is the most favorable, with the highest overall score of 43,153.1, followed by city B and city D, with 25,431.9 and 12,136.4, respectively. This indicates that the education resource allocation effect of these three cities is better and belongs to the upstream level in province Z.

Clinical characteristics, diagnostic methods and results of surgically treated histologically benign cardiac myxomas.

Myxomas are primary cardiac tumours that may be detected incidentally due to embolic events, intracardiac obstructive features or non-specific structural symptoms. The aim of this study was to share our experience of clinical features, diagnostic methods, surgical procedures and postoperative follow up of surgically treated cardiac myxomas. Data of 34 patients who underwent surgery for a cardiac myxoma between January 2006 and June 2022 were retrospectively analysed. Group 1 (n = 19) consisted of patients who were symptomatic and group 2 ( n = 15) patients were asymptomatic. The medical records of the patients, their clinical status, diagnostic methods, operation information and postoperative course data were collected and recorded. A total of 34 patients (16 female; mean age 54.5 ± 8.8 years) underwent cardiac myxoma resection with cardiopulmonary bypass. Fifteen (44.1%) patients were diagnosed incidentally with asymptomatic myxoma. An additional cardiac surgical procedure was performed in six patients (17.7%). The 34 cardiac myxomas that were surgically resected were localised in the left atrium in 25 patients (73.5%) and in the right atrium in nine patients (26.5%). Patients' most common symptoms were dyspnoea (42.1%), palpitations (21.1%), ischaemic stroke (15.8%) and syncope (10.5%). There was no incident of 30-day mortality and no recurrence was observed in any patient during the follow-up period. The duration of surgical intervention in symptomatic patients was significantly shorter than in asymptomatic patients (p = 0.0001), but there was no statistical difference in terms of characteristics. Myxomas are benign tumours, but they are serious pathologies that require early treatment because of signs of obstruction, embolic complications and confusion, with left atrial thrombus in the differential diagnosis.

Dynamics of gonadotropic hormones, immunological status and osteometry data in girls in the region of Uzbekistan in the age aspect

Complex investigation was undertaken on 1064 healthy girls to study parameters of physical and pubertal development age indications of physical and pubertal development of Uzbek girls. This data is correlated with normative indicators of girls living in the European part of the ISU that allowed considering them as standard for all republics. Analysis of the results showed significant redactor of hormonal activity and level of immunologic resistance, as well as high percentage of identification of pathology of reproductive system at girls and young women in Curlen district of Khoresm province that confirms ecologically unfavorable situation in this region of Uzbekistan.

Energy efficiency analysis: A household digital transformation

&lt;p&gt;Nowadays, the increased demand for energy and electrification associated with higher production costs from renewable and cleaner sources has driven up prices, impacting the industrial, commercial, and residential sectors. With a direct influence on the development of these economic sectors, its direct and indirect impacts to products and services have become important to find more efficient ways and best practices on energy use to support sustainable development. Aiming to shed light on this topic, and how individuals and society behave in this energy market transformation, this article explores opportunities for reducing electricity consumption through the use of modern technologies, such as of monitoring, optimization, automation, and adjustment of routines. At the same time, it is also our intention to bring to the surface a discussion around the rational use of everyday resources and raising the awareness of its impact to individuals and institutions. At its core, this work consists of continuous data collection of single devices and equipment in regard to status, energy consumption, and other relevant data of a typical household. Through behavioral changes and introduction of smart home automation techniques, it was possible to trace a parallel comparison between different scenarios and their influence on the energy consumption without negative impact to the comfort of individuals. Seeking a continuous improvement approach, extensive iterations were conducted, and it was possible to notice not only an energy efficiency improvement, but at the same time gains in living standards and safety. The significant results observed over subsequent months and years highlight not only practical and financial benefits, but also increased awareness and behavioral changes toward the rational use of electricity in households.&lt;/p&gt;