Statistical Techniques Research Articles

Nursing students’ perspectives on patients' safety competencies: a cross-sectional survey

BackgroundNurses constitute the largest body of healthcare professionals globally, positioning them at the forefront of enhancing patient safety. Despite their crucial role, there is a notable gap in the literature regarding the comprehension and competency of nursing students in patient safety within Egypt. This gap underscores the urgent need for research to explore how nursing students perceive patient safety and the extent to which these competencies are integrated into their clinical and educational experiences. Understanding these perspectives is essential for developing targeted interventions that can significantly improve patient safety outcomes. The objective of this study was to fill this gap by assessing the perspectives of nursing intern students on patient safety competencies, thereby contributing to the global efforts in enhancing patient safety education and practice.MethodsIn this research, a cross-sectional study design was employed to investigate the topic at hand. A purposive sample of 266 nursing intern students was enrolled from the Faculty of Nursing at Mansoura University. The data were collected using a patient safety survey. Subsequently, the collected data underwent analysis through the application of descriptive and inferential statistical techniques using SPSS-20 software.ResultsAmong the studied intern nursing students, we found that 55.3% and 59.4% of the involved students agreed that they could understand the concept of patient safety and the burden of medical errors. Regarding clinical safety issues, 51.1% and 54.9% of the participating students agreed that they felt confident in what they had learned about identifying patients correctly and avoiding surgical errors, respectively. Concerning error reporting issues, 40.2% and 37.2% of the involved students agreed that they were aware of error reports and enumerated the barriers to incident reporting, respectively. There was a statistically significant difference between the nursing student patient safety overview domain and their age (p = 0.025).ConclusionsOur study's compelling data demonstrated that intern students who took part in the patient safety survey scored higher overall in all patient safety-related categories. However, problems with error reporting showed the lowest percentage. The intern students would benefit from additional educational and training workshops to increase their perspectives on patients' safety competencies.

Efficient optimization of an accelerator neutron source for neutron capture therapy using genetic algorithms.

In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time. Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations. We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB-BNCT. The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR). The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges.

Enhancing technical proficiency through small-sided basketball games: A strategic approach for students athletes

Background Problems: Due to the intricate nature of the game and the requirement for proficiency in core skills like dribbling, passing, shooting, and defensive positioning, basketball players frequently have difficulties developing their technical abilities. Traditional training approaches may not focus enough on these skills, leaving room for growth. Research Objective: This study investigates how well basketball players' technical skills may be improved through game-based instruction, particularly small-sided basketball games. The study aims to address the need for a more focused approach to technical elements by highlighting the value of focused practice in pertinent game scenarios. Methods: The type of research used was quantitative research with a sample of 12 amateur athletes. The instrument used is the game performance evaluation tool (GPET). Quantitative data from pre- and post-assessments were examined using the appropriate statistical techniques, such as paired t-tests or analysis of variance (ANOVA). For the study, basketball players with various skill levels were listed. Small groups of participants received specialised training to develop their technical abilities. Findings and Results: Participants' technical proficiency significantly improved after strategically focused practice using small-sided basketball games. The outcomes show that this game-based method works well for improving basic abilities, including passing, dribbling, shooting, and positioning defensively. The results highlight how effective it is to use small-sided games to help basketball players enhance their skills. Conclusion: The study concludes that small-sided basketball games are an effective strategy for improving basketball players' technical proficiency. Notwithstanding several constraints, such as a comparatively limited sample size and consistency in participant skill levels, the findings indicate that focused practice in pertinent gaming environments produces notable enhancements. To better understand the potential of this strategy for players of different skill levels, future studies should include additional variables like training amounts and differences in small-sided game structures.

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

AbstractExisting studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

Water Quality Assessment Using the Water Quality Index, and Geographic Information Systems in Nador Canal, Morocco

In recent decades, water pollution has emerged as a significant concern, posing a threat to both humans and natural ecosystems. This study aimed to assess the spatial variations in water quality in the Nador Canal, Morocco using the water quality index combined with multivariate statistical techniques and geographic information systems (GIS). The parameters examined were then compared to the maximum permissible limit values recommended by Moroccan surface water standards and the World Health Organization. The results indicated that the WQI of Nador Canal is generally suitable for irrigation and unsuitable for drinking. It indicated that the quality of surface water in the Nador Canal is affected by organic pollutants, and this is evidenced by low levels of dissolved oxygen, the levels of elements were high, especially chlorine and sodium, in addition to a high concentration of ammonia, nitrate, and phosphate in some of the stations studied. Principal component analysis (PCA) results revealed that PC1–PC3 collectively accounted for 73% of the variation in surface water quality within the study area. The cluster analysis also proves that the water quality is relatively polluted. Potential contributing factors to surface water pollution include changes in the hydrological regime, household waste discharges, and agricultural activities. These findings furnish essential insights into water quality, particularly its suitability for irrigation, and contribute to enhancing the water quality management system of the Nador Canal in the Gharb region of Morocco.

SMOOTHER OR BUMPIER RIDE? A STUDY ON TAXPAYER EXPERIENCES WITH COST OF SERVICE AFTER THE GST ROLLOUT

This study explores taxpayers perceptions of the costs of service within the logistics industry after the implementation of Goods and Services Tax (GST). The study showed a significant difference in taxpayers perceptions of service costs across geographical locations and annual turnover. The researchers employed a descriptive research design to examine taxpayers perceptions of service costs post-GST implementation. Convenient random sampling was utilized to ensure a representative sample across various demographic segments. Quantitative data was collected and analyzed using statistical techniques, notably ANOVA, with SPSS software facilitating efficient analysis. While rural areas tend to perceive an increase in service costs, urban areas generally report a reduction. Factors contributing to changes in service costs were analyzed, with increased compliance costs identified as a significant driver of elevated expenses. Conversely, simplifying the input tax credit process effectively reduced service costs. The study aims to offer valuable insights into the impact of GST on the logistics sector by examining taxpayers perceptions of service costs. KEYWORDS: Goods and Services tax, taxpayer perception, cost of services, logistics industry.

On Some Factors Contributing to Entrepreneurial Economic Success in South-West, Nigeria: An Empirical Approach

This essay explores the elements that lead to the financial success of entrepreneurs. In particular, it conducted an empirical investigation into the possibility that factors such as age, religious beliefs, risk-taking, marketing strategies, timely and adequate planning, employees’ commitment, accessibility to working capital, reduced bureaucratic steps on import and export matters, education level, and age and technology infrastructure could influence an entrepreneur’s level of success. The study used a descriptive survey research design. The study’s participants were business owners in the six states that make up Nigeria’s southwest region who operated on a micro, small, and medium scale. The 720 entrepreneurs who made up the study’s sample were chosen through a multi-stage sampling process. A self-structured questionnaire covering all the objectives was used as the data collection tool, and it was administered to a select group of entrepreneurs. In addition to applying non-parametric statistical techniques like the chi-squared test and phi statistic, the collected data were analyzed using descriptive statistics like frequencies, percentages, and graphics. In the R-Studio (Posit) environment, the data analysis operations were carried out. The findings showed that a successful entrepreneurship is greatly influenced by a number of factors, including the availability of operating capital, the degree of employee commitment, technological and infrastructural capabilities, risk-taking, effective marketing strategies, adequate and appropriate planning, educational attainment, and age. Success in entrepreneurship, however, is unrelated to fewer bureaucratic procedures involved in the import and export of goods. It was determined that the bulk of the previously mentioned elements affect any business’s ability to succeed in today’s world. Thus, it was suggested that all tiers of government be counselled to provide funding, among other things, to serious business owners.

Exploration Of Subjective Norms Of Tourists In The Application Of Planned Behavior Theory To Predict The Intention To Visit Tourist Objects In Indonesia

This research aims to explore a deeper understanding of the factors that influence tourists' intentions to visit tourist attractions in Indonesia using the Planned Behavior Theory (TPB) approach. This research focuses on exploring tourists' subjective norms, which is one of the main components in the TPB, along with attitudes and behavioural control. The research method involves an online survey targeting a sample of tourists who have visited or intend to visit tourist attractions in Indonesia. The research instrument consists of structured questions designed to measure critical variables in the TPB, with particular emphasis on subjective norms. Data analysis used descriptive and inferential statistical techniques to identify relationships between key variables. The research findings are expected to significantly contribute to understanding the factors that influence tourists' visiting intentions, as well as their implications for developing marketing strategies and managing tourist attractions in Indonesia. It is hoped that the results of this research can become a reference for tourism industry stakeholders, government and researchers in consumer behaviour and tourism to improve the management and development of tourist attractions in Indonesia to increase the attractiveness and competitiveness of tourist destinations globally.

Experimental Investigation on the 3D Printing of Nylon Reinforced by Carbon Fiber through Fused Filament Fabrication Process, Effects of Extruder Temperature, and Printing Speed

This study investigated how the extruder temperature, printing speed, and specimen geometry interact during a tensile test of continuous carbon fiber-reinforced nylon matrix composites produced by the fused deposition modelling (FDM) process. The investigation utilized statistical techniques. For this purpose, tensile examinations were done on manufactured samples using a testing apparatus. The study’s objective is to identify the most efficient specimen geometry for tensile testing result optimization and to maximize the 3D printing process’s capability for producing complex, freeform patterns in these composites. In this study, the input parameters required for the response surface methodology (RSM) were varying extruder temperature (240-255°C) and printing speed (60-80 mm/s), and experimental responses included modulus, elongation at break, and weight. The findings of the regression analysis showed output responses are influenced by both input variables. The results showed that the strength of the samples was significantly influenced by the input parameters. To draw the surface and residual plots, the software of design expert software was used. The interaction between the two input variables suggests raising the extruder temperature and decreasing printing speed, which leads to printing heavier samples. Inversely, the diversity between the forecasted and real responses for the optimal specimens is less than 10% which is assumed to be acceptable for the design of experiments (DOE). The analysis took into account the lower and upper ranges of the input variable with the goal of enhancing both the most modulus and fracture elongation while simultaneously degrading the weight of the specimens. To achieve this objective, the extruder temperature and printing speed are between 240 and 250°C and 65 and 75 mm/s, respectively.

European Citizens’ Evaluation of the Common Agricultural Policy

The Common Agricultural Policy (CAP) is crucial in ensuring food safety and security in Europe. Providing a stable and diverse food supply also supports rural economies and promotes social, environmental and economic sustainability. However, as CAP consumes approximately 30% of the EU budget, debates regarding its pertinence have arisen. This paper aimed to understand European citizens’ perceptions of CAP, its associated benefits and their level of agreement regarding the allocation of the EU budget. Ultimately, the goal was to gain insights into EU citizens’ attitudes and perceptions towards the CAP. Information and data come from the European Survey Eurobarometer. The methods include statistical inferential techniques adjusted to the nature of the variables and goals. The findings confirm that European citizens generally perceive the CAP positively and have positive attitudes about the CAP. While some differences among various social groups may exist, the results did not identify any specific group strongly opposing the CAP. Overall, European citizens hold a favorable attitude towards the CAP, its associated benefits and budget allocation. This paper brings new insights into new European CAP measures and programs to promote food security and the sustainability of agriculture among European countries and citizens. These insights may be useful for public decision-makers to define better policies and measures adjusted to European citizens and to promote food security and sustainability.

Stability and statistical inversion of travel time tomography

Abstract In this paper, we consider the travel time tomography problem for conformal metrics on a bounded domain, which seeks to determine the conformal factor of the metric from the lengths of geodesics joining boundary points. We establish forward and inverse stability estimates for simple conformal metrics under some a priori conditions. We then apply the stability estimates to show the consistency of a Bayesian statistical inversion technique for travel time tomography with discrete, noisy measurements.

Combining statistical dialog management and intent recognition for enhanced response selection

Abstract Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.

Sectorial evaluation and characterization of internal marketing orientation through multicriteria analysis

This research carries out a comparative analysis of the Internal Market Orientation (IMO) to a sample of small and medium enterprise companies, which has been divided into nine groups based on their business sectors. Applying the statistical technique of structural equations, the coefficients of four IMO inputs in each business sector are obtained. Next, the PROMETHEE multicriteria technique is applied to obtain a composite indicator of the IMO implementation of each business sector. The value of the composite indicator together with the GAIA plane allow to distinguish the differences of the IMO profiles, their strengths and their weaknesses. The results show the divergences in the IMO inputs according to the sector, which is evidence that the business sector is a predictor of the IMO implementation of a company.

Connectedness of cryptocurrency markets to crude oil and gold: an analysis of the effect of COVID-19 pandemic

The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.

Impact of tumor size on overall survival and cancer-specific survival of early-onset colon and rectal cancer: a retrospective cohort study

PurposeThis study aimed to investigate the impact of tumor size on survival in early-onset colon and rectal cancer.MethodsEarly-onset colon and rectal cancer patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Tumor size was analyzed as both continuous and categorical variables. Several statistical techniques, including restricted cubic spline (RCS), Cox proportional hazard model, subgroup analysis, propensity score matching (PSM), and Kaplan–Meier survival analysis, were employed to demonstrate the association between tumor size and overall survival (OS) and cancer-specific survival (CSS) of early-onset colon and rectal cancer.ResultsSeventeen thousand five hundred fifty-one (76.7%) early-onset colon and 5323 (23.3%) rectal cancer patients were included. RCS analysis confirmed a linear association between tumor size and survival. Patients with a tumor size > 5 cm had worse OS and CSS, compared to those with a tumor size ≤ 5 cm for both early-onset colon and rectal cancer. Notably, subgroup analysis showed that a smaller tumor size (≤ 50 mm) was associated with worse survival in stage II early-onset colon cancer, although not statistically significant. After PSM, Kaplan–Meier survival curves showed that the survival of patients with tumor size ≤ 50 mm was better than that of patients with tumor size > 50 mm.ConclusionPatients with tumors larger than 5 cm were associated with worse survival in early-onset colon and rectal cancer. However, smaller tumor size may indicate a more biologically aggressive phenotype, correlating with poorer survival in stage II early-onset colon cancer.

The Role of AI in Management

This study investigates "The Role of AI in Management" and aims to understand the impact of AI technologies on various management practices within organizations. The problem of the study arises from the increasing adoption of AI in the business world and the need to comprehend its implications for managerial decision-making, organizational efficiency, and employee experiences. The study focuses on both theoretical and practical aspects, providing insights that can inform academic research and guide organizations in leveraging AI effectively. To address the research problem, a cross-sectional study design using a questionnaire as the primary data collection tool has been selected. This design allows for the collection of quantitative data at a specific point in time, providing a snapshot of the relationship between AI and management practices. The questionnaire will be administered to professionals and managers across diverse industries, representing organizations that have implemented AI technologies in their management processes. The importance of this study lies in its theoretical and practical contributions. Theoretically, it expands the understanding of the role of AI in management by examining its impact on decision-making processes, organizational structures, and employee roles. It contributes to the existing body of knowledge by exploring the nuances and complexities of AI implementation in managerial contexts. Practically, the findings will provide insights and recommendations for organizations seeking to adopt or optimize AI technologies in their management practices. It will guide decision-makers in understanding the potential benefits, challenges, and best practices associated with AI integration. The scope of this study encompasses professionals and managers across various industries, ensuring a diverse representation of AI adoption in management practices. The focus is on organizations that have implemented AI technologies and are actively utilizing them in their decision-making processes, resource allocation, and performance evaluation. The study aims to capture the perceptions, experiences, and attitudes of participants related to the role of AI in management. Data will be collected through a structured questionnaire distributed electronically to the selected participants. The questionnaire will capture quantitative data on participants’ perceptions, experiences, and attitudes towards AI in management. It will include closed-ended and Likert-scale questions, enabling efficient data collection and analysis. The collected data will be analyzed using appropriate statistical techniques, including descriptive statistics, correlation analysis, and regression analysis. By employing this methodology and research tools, this study seeks to provide a comprehensive understanding of the impact of AI on management practices. The findings will contribute to both theoretical knowledge and practical insights, guiding organizations in leveraging AI effectively to enhance decision-making, improve operational efficiency, and foster innovation in management processes.

A Method of Skin Disease Detection Using Image Processing and Machine Learning

Skin conditions are prevalent health issues globally. The dangers posed by these infections are unseen, leading to physical discomfort and mental anguish. In severe cases, skin diseases can even progress to skin cancer. Consequently, the identification of skin diseases from clinical images remains a significant challenge in medical image analysis. Furthermore, manual diagnosis by medical professionals is time-consuming and subjective. Therefore, there is a need for automated skin disease prediction to expedite treatment planning for both patients and dermatologists. These studies presents a digital hair removal method and de-blurs or denoise the images. To extract essential patterns from the skin imagesand statistical features techniques are utilized. Two efficient machine learning algorithms, namely Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classifiers, are employed with the extracted features to accurately classify skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using the ISIC 2019 challenge and HAM10000 datasets, with SVM demonstrating slightly superior performance compared to the other classifiers. Furthermore, a comparison with state-of-the-art methods is conducted to evaluate the effectiveness of the proposed approach.

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.

Assessment of the Efficiency of a Hybrid Photovoltaic and Photovoltaic Heating System (PV–Solar) in the Context of a Warehouse for a Housing Community in Poland

In light of global challenges such as the war in Ukraine and the depletion of fossil fuel resources, it is essential to explore sustainable energy solutions. Hybrid energy systems represent a potential solution, offering energy independence to urban housing estates and reducing CO2 emissions. This article aims to explore the feasibility of integrating photovoltaic systems (utilizing vacuum collectors) and combined utilities (system heat and electricity) in a hybrid setup, leveraging existing technical infrastructure with necessary modifications. A key aspect is to perform calculations on the amount of heat and electricity generated from these systems. The study analyzes the demand for heat and electricity among consumers compared to the estimated production from renewable sources. Calculations also include the potential energy savings and CO2 emission reductions achievable through the proposed solutions. The findings indicate that hybrid photovoltaic systems with heat storage could effectively address energy issues in urban housing estates, given adequate support and community involvement. The innovative methodology employed in this study encompasses both analytical and experimental research approaches. The analysis employs advanced statistical techniques and data integration to enhance understanding of the phenomena studied, while the experimental research provides robust results through controlled variable manipulation and precise measurement tools, thereby verifying the study’s objectives.