bluuebunny/crossref_metadata_2025 huggingface dataset:
Contemporary Management of Renal Cell Carcinoma: A Review for General Practitioners in Oncology
Authors: Tejura Anish , Fernandes Ricardo , Hubay Stacey , Ernst Matthew Scott , Valdes Mario , Batra Anupam
Published: August 2024

Renal cell carcinoma accounts for a significant proportion of cancer diagnoses in Canadians. Over the past several years, the management of renal cell cancers has undergone rapid changes in all prognostic risk categories, resulting in improved oncologic outcomes. Novel strategies for metastatic disease make use of the synergy between checkpoints and angiogenesis inhibition. Moreover, combination checkpoint inhibition has demonstrated durable efficacy in some patients. Adjuvant immunotherapy has recently shown a survival benefit for the first time in select cases. Significant efforts are underway to explore new compounds or combinations for later-line diseases, such as inhibitors of hypoxia-inducible factors and radiolabeled biomolecules targeting tumor antigens within the neoplastic microenvironment for precise payload delivery. In this manuscript, we provide a comprehensive review of the available data addressing key therapeutic areas pertaining to systemic therapy for metastatic and localized disease, review the most relevant prognostic tools, describe local therapies and management of CNS disease, and discuss practice-changing trials currently underway. Finally, we focus on some of the practical aspects for general practitioners in oncology caring for patients with renal cell carcinoma.
Estrogen Regulated Genes Compel Apoptosis in Breast Cancer Cells, Whilst Stimulate Antitumor Activity in Peritumoral Immune Cells in a Janus-Faced Manner
Authors: Suba Zsuzsanna
Published: August 2024

Background: Breast cancer incidence and mortality exhibit a rising trend globally among both premenopausal and postmenopausal women, suggesting that there are serious errors in our preventive and therapeutic measures. Purpose: Providing a series of valuable, but misunderstood inventions highlighting the role of increasing estrogen signaling in prevention and therapy of breast cancer instead of its inhibition. Results: 1. Breast cells and breast cancer cells with germline BRCA1/2 mutations similarly show defects in liganded estrogen receptor (ER) signaling, demonstrating its role in genomic instability and cancer initiation. 2. In breast tumors, the increased expression of special receptor family maybe an effort for self-directed improvement of genomic defects, while the weakness or loss of receptors indicates a defect requiring medical repair. 3. ER overexpression in breast cancer cells is capable of strengthening estrogen signaling and DNA repair, while in ER negative tumors, HER2 overexpression tries to upregulate unliganded ER activation and genome stabilization. 4. ER-positive breast cancers responsive to endocrine therapy may show a compensatory ER overexpression resulting in a transient tumor response. Breast cancers non-responsive to antiestrogen treatment exhibit HER2-overexpression for compensating the complete inhibition of hormonal ER activation. 5. In breast tumors, somatic mutations serve upregulation of ER activation via liganded or unliganded pathway helping genome stabilization and apoptotic death. 6. The mutual communication between breast cancer and its inflammatory environment is a wonderful partnership among cells fighting for genome stabilization and apoptotic death of tumor. 7. In breast cancers, there is no resistance to genotoxic or immune blocker therapies, but rather, the nonresponsive tumor cells exhaust all compensatory possibilities against therapeutic damages. Conclusions: Understanding the behavior and ambition of breast cancer cells may achieve a turn in therapy via applying supportive care instead of genotoxic measures.
Association of Cancer with Heart Failure and the Prognostic Value of NT-proBNP in Cancer Patients: Findings from the NHANES (1999–2018)
Authors: Zeng Qingping , Chang Weihong , Zhang Rui , Fan Hongxuan , Dou Zixuan , Liu Aman , Yu Jie , Zhou Boda
Published: August 2024

Evidence regarding the association between cancer and heart failure (HF) is scarce. This study is to investigate the association between HF and cancer and explore the prognostic value of NT-proBNP in cancer patients. This cohort study used National Health and Nutrition Examination Survey data from 1999 to 2018 and linked mortality information until 2019. We included all participants with valid answer to questions regarding self-reported cancer and HF. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% CIs. Our study included data from 54,847 adult participants. During a median (IQR) follow-up of 9.6 (4.0–15.1) years, 7674 deaths were recorded. HF was associated with an increased occurrence of cancer after propensity score matching (OR = 1.46, 95% CI: 1.17–1.82, p < 0.001). Cancer was associated with a higher occurrence of HF (OR = 1.33, 95% CI: 1.11–1.59, p = 0.002). Kaplan–Meier survival analysis over 10 years revealed the shortest survival in patients with both HF and cancer (log-rank p < 0.0001). Importantly, NT-proBNP was significantly higher in cancer patients, no matter whether with known HF (p < 0.01). In cancer patients without HF, NT-proBNP higher than 51.51 pg/mL was associated with shorter survival (log-rank p < 0.0001). Findings from this cohort study suggest that HF is significantly associated with cancer. NT-proBNP was higher in cancer patients, with significant prognostic value in cancer patients.
A Comparison of Butterfly Diversity Results between iNaturalist and Expert Surveys in Eastern Oklahoma
Authors: Harman Alexander J. , Eori Madeline M. , Hoback W. Wyatt
Published: August 2024

Ongoing worldwide biodiversity declines and range shifts associated with climate change increase the importance of documenting the current distributions of species to establish baseline data. However, financial and logistical constraints make it impossible for taxonomic experts to conduct thorough surveys in most locations. One popular approach to offset the lack of expert sampling is using community science data collected by the public, curated, and made available for research. These datasets, however, contain different biases than those typically present in data collected through conventional survey practices, often leading to different results. Recent studies have used massive datasets generated over large areas; however, less is known about the results obtained at smaller scales or with more limited sampling intervals. We compared butterfly observations in eastern Oklahoma using a dataset obtained from the popular community science website iNaturalist and one collected during targeted surveys of glade habitats conducted by taxonomic experts. At the county-level scale, the relative abundances of butterfly species correlated well between the glade surveys and the iNaturalist observations, and there was no difference in the relative abundance of different butterfly families between the two survey methods. However, as anticipated, the conventional surveys outperformed the community science data in measuring biodiversity at a smaller geographic scale.
Accuracy of Gingival Crevicular Fluid Biomarkers of MMP8, TIMP1, RANK, RANKL, and OPG in Differentiating Symptomatic and Asymptomatic Apical Periodontitis
Authors: Abdulhadi Zeena Tariq , Mahdee Anas Falah , Gul Sarhang Sarwat
Published: August 2024

Apical periodontitis (AP) is the most prevalent chronic inflammatory disease of the teeth. Bone resorption dynamics in symptomatic and asymptomatic AP are still unrecognized. This study examined different inflammatory markers within gingival crevicular fluid, including matrix metalloproteinases 8 (MMP8), tissue inhibitors of metalloproteinases 1 (TIMP1), receptor activator of nuclear factor κB (RANK), its ligand (RANKL), and osteoprotegerin (OPG), to be used in comparing symptomatic apical periodontitis (SAP) and asymptomatic apical periodontitis (AAP) versus healthy teeth. Subjects with SAP, AAP, and a control group were recruited and GCF samples were collected by Periopaper strips. Clinical and radiographical measures were used for diagnosing AP. Levels of MMP8, TIMP, RANK, RANKL, and OPG were determined by ELISA and their abilities to discriminate between examined sites were evaluated by receiver operator characteristic (ROC) curves. All examined biomarkers were statistically significant higher (p < 0.05) in SAP than AAP and the control group, apart from RANK. Significant positive correlations (p < 0.05) were identified between all SAP and AAP biomarkers except TIMP1 and RANK in AAP teeth. TIMP1 and OPG exhibited the highest ability to distinguish between SAP and AAP with areas under the curve of 0.824 and 0.763 in comparing SAP and the control group, and 0.732 and 0.73 when comparing AAP and the control group, respectively. Additionally, TIMP1 and OPG showed the highest AUC of 0.778 and 0.747 when SAP and AAP were compared, respectively. This study concluded that GCF levels of TIMP1 and OPG can be used to differentiate between SAP, AAP, and healthy teeth.
Multi-UAV Path Planning Based on Cooperative Co-Evolutionary Algorithms with Adaptive Decision Variable Selection
Authors: Meng Qicheng , Qu Qingjun , Chen Kai , Yi Taihe
Published: August 2024

When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased number of decision variables. Moreover, the complex interrelationships between the decision variables of each UAV add to the challenge of selecting appropriate decision variables. To tackle this problem, we propose a novel collaborative algorithm called CCEA-ADVS. This algorithm reduces the difficulty of the problem by decomposing high-dimensional variables into sub-variables for collaborative optimization. To improve the efficiency of decision variable selection in the collaborative algorithm and to accelerate the convergence speed, an adaptive decision variable selection strategy is introduced. This strategy selects decision variables according to the order of solving single-UAV constraints and multi-UAV constraints, reducing the cost of the optimization objective. Furthermore, to improve computational efficiency, a two-stage evolutionary optimization process from coarse to fine is adopted. Specifically, the Adaptive Differential Evolution with Optional External Archive algorithm (JADE) is first used to optimize the waypoints of the UAVs to generate a basic path, and then, the Dubins algorithm is combined to optimize the trajectory, yielding the final flight path. The experimental results show that in four different scenarios involving 40 UAVs, the CCEA-ADVS algorithm significantly outperforms the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and JADE algorithms in terms of path performance, running time, computational efficiency, and convergence speed. In addition, in large-scale experiments involving 500 UAVs, the algorithm also demonstrates good adaptability, stability, and scalability.
Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data
Authors: Niu Yan , Xiang Jie , Gao Kai , Wu Jinglong , Sun Jie , Wang Bin , Ding Runan , Dou Mingliang , Wen Xin , Cui Xiaohong , Zhou Mengni
Published: August 2024

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn’s sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.
Event-Oriented State Alignment Network for Weakly Supervised Temporal Language Grounding
Authors: Wu Hongzhou , Zhang Xiang , Tang Tao , Yang Canqun , Luo Zhigang
Published: August 2024

Weakly supervised temporal language grounding (TLG) aims to locate events in untrimmed videos based on natural language queries without temporal annotations, necessitating a deep understanding of semantic context across both video and text modalities. Existing methods often focus on simple correlations between query phrases and isolated video segments, neglecting the event-oriented semantic coherence and consistency required for accurate temporal grounding. This can lead to misleading results due to partial frame correlations. To address these limitations, we propose the Event-oriented State Alignment Network (ESAN), which constructs “start–event–end” semantic state sets for both textual and video data. ESAN employs relative entropy for cross-modal alignment through knowledge distillation from pre-trained large models, thereby enhancing semantic coherence within each modality and ensuring consistency across modalities. Our approach leverages vision–language models to extract static frame semantics and large language models to capture dynamic semantic changes, facilitating a more comprehensive understanding of events. Experiments conducted on two benchmark datasets demonstrate that ESAN significantly outperforms existing methods. By reducing false high correlations and improving the overall performance, our method effectively addresses the challenges posed by previous approaches. These advancements highlight the potential of ESAN to improve the precision and reliability of temporal language grounding tasks.
Positive or Negative and General or Differentiated Effect? Correlation between Parental Involvement and Student Achievement
Authors: Bacskai Katinka , Alter Emese , Dan Beáta Andrea , Vályogos Krisztina , Pusztai Gabriella
Published: August 2024

Parents can have a significant impact on student progress, but it is unclear whether generally influential forms of parental involvement can be identified or if they have a differential impact. Our research focuses on the impact of different forms of parental involvement (at-home and in-school involvement) on primary and high school students’ academic performance, and we also examine the differences between low- and high-status families. Our quantitative research analyzed data from all students that were in Grade 6 and Grade 10 in Hungary in 2019, and we conducted analysis using the Hungarian National Competency Assessment database (n = 183.366). We examined how parental involvement affects children’s mathematics and reading achievements. The results of our research were as follows: (1) Parental involvement type impacts student achievement. Home-based PI has a negative effect, while teacher–parent conferences and discussing school events have a positive effect. (2) The link between parental involvement and student achievement is weaker for higher-status families compared to lower-status families. Based on our results, the generalizability of the positive impact of parental involvement can be questioned because not all forms of it have a favorable effect on student achievement.
School Leaders’ Well-Being during Times of Crisis: Insights from a Quantitative Study in Kazakhstan
Authors: Durrani Naureen , Makhmetova Zhadyra
Published: August 2024

Amidst increasing global pressures on school leaders, particularly during crises when unforeseen situations necessitate prompt and decisive action from them, understanding the multifaceted dimensions of their well-being is essential for ensuring effective leadership and maintaining educational quality. This study examines the well-being of school leaders during crises by analysing survey responses from 1299 school leaders in Kazakhstan. Employing a well-being framework comprising dispositional, relational, and contextual dimensions, various predictors were assessed within each dimension. Gender biases (dispositional), challenges such as managing conflicts with parents (relational), and limited school autonomy and digital infrastructure (contextual) were identified as factors negatively impacting school leaders’ well-being. Notably, gender significantly influences well-being, particularly impacting female school leaders. School location and medium of instruction are not associated with well-being, implying the pervasive impact of remote schooling on school leaders’ well-being. Findings underscore the importance of integrating crisis management courses in professional development, implementing policy initiatives to handle contextual factors like equitable resource distribution and increased school autonomy, promoting self-care practices, and advocating for gender perspectives in institutional policies to bolster support for women school leaders.
Multihead-Res-SE Residual Network with Attention for Human Activity Recognition
Authors: Kang Hongbo , Lv Tailong , Yang Chunjie , Wang Wenqing
Published: August 2024

Human activity recognition (HAR) typically uses wearable sensors to identify and analyze the time-series data they collect, enabling recognition of specific actions. As such, HAR is increasingly applied in human–computer interaction, healthcare, and other fields, making accurate and efficient recognition of various human activities. In recent years, deep learning methods have been extensively applied in sensor-based HAR, yielding remarkable results. However, complex HAR research, which involves specific human behaviors in varied contexts, still faces several challenges. To solve these problems, we propose a multi-head neural network based on the attention mechanism. This framework contains three convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The model uses a channel attention module (squeeze–excitation module) to enhance the representational capabilities of convolutional neural networks. We conducted experiments on two publicly available benchmark datasets, UCI-HAR and WISDM, to evaluate our model. The results were satisfactory, with overall recognition accuracies of 96.72% and 97.73% on their respective datasets. The experimental results demonstrate the effectiveness of the network structure for the HAR, which ensures a higher level of accuracy.
Thin Film Semiconductor Metal Oxide Oxygen Sensors: Limitations, Challenges, and Future Progress
Authors: Bulowski Wojciech , Knura Rafał , Socha Robert P. , Basiura Maciej , Skibińska Katarzyna , Wojnicki Marek
Published: August 2024

Among oxygen sensors, types such as polymer-, ceramic-, or carbon-based ones may be distinguished. Particular interest in semiconductor metal oxide (SMO) sensors has recently been observed. This is due to their easy fabrication process, high control over the final product (dopants, posttreatment, etc.), and high concentration of oxygen vacancies, by which they show significant changes in electrical properties when exposed to analyte. In this review, different types of sensors are described and categorized. Importantly, their limitations, challenges and principles of sensing mechanism are also discussed, wherein attention is primarily paid to semiconductor metal oxide (SMO) oxygen sensors. This comprehensive review provides an in-depth analysis of the existing literature on planar SMO oxygen sensors, focusing on various materials, fabrication techniques, and sensing mechanisms. It also critically assesses the challenges and limitations in current research, offering insights into future directions for developing highly efficient and reliable sensors. Currently, most oxygen resistive sensors are a few micrometers thick and operate at high temperatures, which leads to high power consumption. To highlight importance of this topic, a market overview is also presented.
DAN-YOLO: A Lightweight and Accurate Object Detector Using Dilated Aggregation Network for Autonomous Driving
Authors: Cui Shuwan , Liu Feiyang , Wang Zhifu , Zhou Xuan , Yang Bo , Li Hao , Yang Junhao
Published: August 2024

Object detection is becoming increasingly critical in autonomous driving. However, the accuracy and effectiveness of object detectors are often constrained by the obscuration of object features and details in adverse weather conditions. Therefore, this paper presented the DAN-YOLO vehicle object detector specifically designed for driving conditions in adverse weather. Building on the YOLOv7-Tiny network, SPP was replaced with SPPF, resulting in the SPPFCSPC structure, which enhances processing speed. The concept of Hybrid Dilated Convolution (HDC) was also introduced to improve the SPPFCSPC and ELAN-T structures, expanding the network’s receptive field (RF) while maintaining a lightweight design. Furthermore, an efficient multi-scale attention (EMA) mechanism was introduced to enhance the effectiveness of feature fusion. Finally, the Wise-IoUv1 loss function was employed as a replacement for CIoU to enhance the localization accuracy of the bounding box (bbox) and the convergence speed of the model. With an input size of 640 × 640, the DAN-YOLO algorithm proposed in this study achieved an increase in mAP0.5 values of 3.4% and 6.3% compared to the YOLOv7-Tiny algorithm in the BDD100K and DAWN benchmark tests, respectively, while achieving real-time detection (142.86 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed under adverse driving conditions, indicating the suitability for autonomous driving applications.
Integrated Energy Systems Modeling with Multi-Criteria Decision Analysis and Stakeholder Engagement for Identifying a Sustainable Energy Transition
Authors: Davidsdottir Brynhildur , Ásgeirsson Eyjólfur Ingi , Fazeli Reza , Gunnarsdottir Ingunn , Leaver Jonathan , Shafiei Ehsan , Stefánsson Hlynur
Published: August 2024

The aim of this paper is to present a decision support system (DSS) to capture the complexity of the transition of a national energy system to net zero in the context of multiple sustainability themes. The paper proposes an integrated assessment framework that combines dynamic systems modeling, sustainability indicators, and multi-criteria decision analysis (MCDA) with direct stakeholder involvement. To illustrate the use of the DSS, the paper compares bundles of climate change policies that aim to decarbonize the road transport sector in Iceland. Eighteen scenarios and alternative development trajectories are defined for the Icelandic energy system based on a combination of three main driving forces. These are, firstly, economic development (three cases); secondly, changes in energy efficiency (two cases); and finally, three climate policy bundles aimed at increasing the share of electric vehicles. Based on the results from the integrated assessment framework, the performance scores of the climate policy bundles are compared across the following five sustainability themes: social impact; economic development; environmental impact; energy security; and technical aspects. The findings confirm that a different conclusion may be reached when multiple sustainability themes are applied in the selection of preferred policy bundles as compared to conventional techno-economic criteria. Banning the registration of fossil-fueled vehicles, combined with economic instruments, offers the best decarbonizing strategy to reach climate and energy policy goals simultaneously.
Contemporary and Conventional Passive Methods of Intensifying Convective Heat Transfer—A Review
Authors: Kozłowska Ewa , Szkodo Marek
Published: August 2024

The ever-increasing demand for effective heat dissipation and temperature control in industrial and everyday applications highlights a critical research problem. The need for development is not only in terms of providing thermal comfort to humans but also forms the basis for the efficient operation of machines and equipment. Cooling of industrial machinery and household electronic equipment is a crucial element in any manufacturing process, and the planning and design of appropriate cooling systems continues to be an integral part of the machine design and construction process. Manufacturers aim to maximize performance while minimizing size and weight. This article reviews widely used passive methods to enhance heat transfer, focusing on their effectiveness in improving convective heat transfer. The techniques examined include surface modifications and advanced materials like foamed metals and nanostructured coatings, which influence turbulence and heat transfer coefficients. The key findings demonstrate that surface roughness, perforated fins, and twisted tapes enhance fluid mixing but may increase flow resistance. The review underscores the significance of these passive methods in optimizing cooling system efficiency across various applications. Despite the variety of techniques available, many areas, especially those involving laser beam modifications, remain underexplored, indicating a need for further research in this field.
Effect of Governmental Subsidies on Green Energy Supply Chains: A Perspective of Meteorological Disasters
Authors: Liang Shan , Zhang Huiming , Zhang Tianyi
Published: August 2024

The energy sector, serving as a pivotal propellant within the macroeconomic framework, finds its production, distribution, and consumption aspects considerably influenced by climatic variations. In this study, a two-tier Stackelberg model for the green energy supply chain is developed, which is employed to analyze the profit disparities between suppliers and retailers both in the absence and presence of meteorological disasters. Furthermore, the research delves into the optimal supply chain strategies under three distinct scenarios in the context of meteorological disasters. A comparative analysis is conducted to examine the decision-making variances and the shifts in the interests of each supply chain participant under these scenarios. The findings reveal two critical insights: First, meteorological disasters detrimentally affect the environmental friendliness of energy products, as well as the profits of both retailers and suppliers. Second, the selection of the optimal strategy varies depending on the evaluation criterion used. When product greenness is the metric, subsidies aimed at promoting sales costs emerge as the superior strategy. Conversely, for retailers and suppliers, subsidies that target the environmental friendliness of energy products prove to be the most effective. Based on the conclusions, the paper also makes recommendations for governments and companies.
Importance of Window Installation in Residential Building Envelopes Having Continuous External Insulation in Order to Realize Energy Efficiency
Authors: Shah Bipin , Bhandari Mahabir , Tang Mengjia
Published: August 2024

Residential buildings are one of the prime candidates in the United States for reducing energy consumption. Continuous exterior insulation (CEI) is being used increasingly often in residential buildings to improve energy efficiency. Windows constitute 15–40% of a building envelope and are the weakest component in energy performance. The installation of windows in walls with CEI has not been well evaluated. We identified four cases of installing windows in walls with CEI of 25–76 mm (1–3 in.) thickness and analyzed the energy loss between the window and wall interface (flanking loss), structural issues, air leakage, and moisture penetration. Thermal analysis showed that the insulation value (RSI) of the 305 mm (12 in.) perimeter wall surrounding a window decreased by 7.6–34.5% in the four cases when compared with the RSI of the wall without the window. A window installation method is proposed to address the issues likely to occur with installation methods currently being used in the field. An out-of-the-box installation system was also designed to achieve a better thermal performance, cost effectiveness, and structural performance in high-performance residential buildings.
AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings
Authors: Ali Dalia Mohammed Talat Ebrahim , Motuzienė Violeta , Džiugaitė-Tumėnienė Rasa
Published: August 2024

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively).
Impact of Steep Seabed Terrains on Oscillating Buoy-Wave Energy-Converter Performance
Authors: Wang Zhenpeng , Lv Changqi , Sheng Songwei , Chen Min , Yang Xianyuan , Wang Wensheng
Published: August 2024

This paper employs Computational Fluid Dynamics (CFD) methods to develop a numerical model of an oscillating buoy-wave energy converter and investigates the impact of steep seabed topography near islands and reefs on its performance. The model’s accuracy is validated by comparison with experimental results from the published literature. Subsequently, the influence of deployment location, reef-front slope gradient, and reef-flat water depth on the device’s performance is analyzed. The results indicate that the strategic utilization of steep seabed topography can significantly enhance the energy capture efficiency of the device in long-wave regions. This study provides valuable references for the design and deployment of oscillating buoy-wave energy converters in near-reef areas.
An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting
Authors: Liu Hengyu , Xing Zuoxia , Zhao Qingqi , Liu Yang , Zhang Pengfei
Published: August 2024

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.
Process Modelling of Integrated Bioethanol and Biogas Production from Organic Municipal Waste
Authors: Gegić Brankica , Vučurović Damjan , Dodić Siniša , Bajić Bojana
Published: August 2024

One of the key guidelines in the European waste management policy is the diversion of waste from landfills, preventing harmful effects on human health and the environment and ensuring that economically valuable waste materials are efficiently recycled and reused through proper management. The organic fraction of municipal waste is abundant and contains biodegradable ingredients such lignocellulose, starch, lipids, pectin, and proteins, making it suitable for biotechnological production. Taking into account that a large amount of organic waste is disposed of in landfills, within this work, the amount of organic waste disposed of in the landfill in Banja Luka was considered. Four simulation model scenarios of the integrated production of bioethanol and biogas are generated, and their process and economic aspects are discussed. In the first two modelled scenarios, the pretreatment conditions (1% sulfuric acid and a different neutralization agent) were varied, while in the other two, the share of the amount of raw material used for the production of bioethanol, i.e., biogas, was varied (split factor: 10–90%). The modelled plant, with a designed capacity of 6 tons/h of organic waste, is a significant bioethanol producer, generating 5,000,000 L/year. The profitability indicators, when examined, revealed that dedicating a portion of the organic municipal waste input exclusively to biogas production leads to decreased process efficiency. Based on the modeled process parameters, ethanol’s minimum feasible selling price is $0.6616 per liter, while regarding the composition of organic municipal waste, carbohydrates have the most significant impact on the viability of the process. The developed model represents an excellent basis for further development of this integrated bioprocess in such a way that it can be modified with new process parameters or economic or ecological indicators and used at all levels of bioprocess design. Additionally, the obtained sustainable integrated bioethanol and biogas production plant models could support forthcoming steps in municipal waste management by providing reliable data on the conditions under which the integrated process of bioethanol and biogas production would take place, as well as the technical feasibility and economic profitability of such organic municipal waste utilization.
The “Pink Tax” and Gender Price Disparity in Personal Care
Authors: Wishart Grace , Poo Mark Ching-Pong , Baxter Katherine , Lau Yui-yip
Published: August 2024

The “Pink Tax” refers to charging higher prices for products and services marketed primarily to women compared to similar or identical items targeted at men, despite comparable production costs. This research uses Unilever as a case study to focus on the impact of the Pink Tax on women’s purchasing power, particularly within the personal care industry. The study provides a comprehensive overview of the Pink Tax’s evolution, key research findings, public responses, and regulatory measures to address this issue. By synthesising existing research and case studies, this paper highlights the economic burden imposed on women and the reinforcement of gender stereotypes through market practices. The study underscores the need for equitable pricing mechanisms and informed consumer advocacy to rectify the economic disparities exacerbated by the Pink Tax.
Some Linguistic Aspects of the Term “Statistics”
Authors: Missiakoulis Spyros
Published: August 2024

The origin of the word “Statistics” is mistakenly sought in the Latin word “Status”. Although the term is indeed etymologically linked to “Status”, it is derived from the Greek noun “Στατός” (statos), meaning standing.
Risk Factors and Preventive Measures for Lung Cancer in the European Union
Authors: Kehrle Katharina , Hetjens Michael , Hetjens Svetlana
Published: August 2024

Background: Lung cancer is worldwide one of the most common types of cancer with still very high mortality rates. The aim of this study was to identify and demonstrate correlations between lung cancer mortality rates and potential influencing factors in EU countries. Methods: This retrospective study investigated the connections between the mortality rates in the EU countries (n = 28) and potential influencing factors. The significant factors from the correlation analysis were identified using a stepwise multiple regression analysis. Results: The most important factors for both genders are the incidence of lung cancer, the price of tobacco, and the number of doctors per 100,000 inhabitants. Conclusion: Lung cancer is a significant global health challenge. The study identified potential strategies for reducing the mortality rate from lung cancer. These strategies include an increase in the number of physicians, enhanced accessibility to cutting-edge antineoplastic medications, and state-funded coverage of the associated costs. It would be beneficial for politicians to consider implementing LDCT screening for the early detection of the disease. The implementation of uniform healthcare system optimization across the EU, combined with improvements in socio-economic conditions, has the potential to mitigate the risk of developing lung cancer.
Using Headspace Gas Chromatography–Mass Spectrometry to Investigate the Volatile Terpenoids Released from the Liquidambar formosana Leaf and Its Essential Oil
Authors: Chang Yu-Yi , Huang Yu-Mei , Chang Hui-Ting
Published: August 2024

Phytoncides and aromatherapy scents mainly derive from plant secondary metabolites and are now well known for their health benefits. In this study, we analyzed the chemical composition of the leaf-derived essential oil of Liquidambar formosana (Altingiaceae) using GC-MS; we also investigated the VOCs released from L. formosana leaves and the leaf essential oil at different temperatures by means of headspace gas chromatography–mass spectrometry (HS-GC-MS). Regarding the VOCs of the leaves, monoterpenes predominated the VOCs at both temperatures, mainly comprising sabinene, followed by γ-terpinene, α-terpinene, and α-pinene. The intensity of the leaf VOCs at 50 °C was nearly three times higher than that at 25 °C; the emission of monoterpenes significantly increases at higher environmental temperatures. The VOC emissions of oxygenated monoterpenes from the leaf essential oil increased at higher temperatures (50 °C), especially those of terpinen-4-ol. Our results reveal that HS-GC-MS can be used to conveniently and directly analyze the VOCs emitted from L. formosana leaves and their essential oils and to evaluate the influence of temperature on the composition of the VOCs of specimens. These VOC studies will assist in the sustainable development and utilization of L. formosana trees for forest therapy, as well as the use of their leaf essential oil for aromatherapy.
Fine Roots in Hemiboreal Forest Stands and Clearcut Areas with Nutrient-Rich Organic Soils in Latvia: Morphological Traits, Production and Carbon Input
Authors: Lazdiņš Andis , Petaja Guna , Bārdule Arta , Polmanis Kaspars , Kalēja Santa , Maliarenko Oksana , Melnik Nadiia
Published: August 2024

Within this study, we evaluated the fine root (trees and understory vegetation combined) morphological traits, fine root production (FRP), and carbon (C) input with fine root litter in forest stands (dominated by either coniferous or deciduous trees) and clearcut areas (previously dominated by coniferous trees) with nutrient-rich organic soils. The study was conducted in 26 sites in hemiboreal forest land in Latvia and summarizes the results obtained in a two-year study (2020–2022) using the root ingrowth method. Traits and production of fine roots varied significantly depending on forest development stage (stand or clearcut area), dominant tree species type (coniferous or deciduous), and soil drainage status (drained or naturally wet). According to the results of the second study year, mean FRP among groups of study sites varied from 0.58 ± 0.13 to 1.38 ± 0.28 t ha−1 yr−1, while C input with fine root litter ranged from 0.28 ± 0.06 to 0.68 ± 0.14 t C ha−1 yr−1. More than half (59 ± 4%) of the total FRP occurred in the upper 0–20 cm soil layer. FRP tended to correlate positively with soil C/N ratio and negatively with soil pH and soil nutrient concentration. Incubating ingrowth cores for at least two years is strongly recommended to accurately estimate annual FRP and C input. This helps to avoid potential underestimation that may occur when using results of only one incubation year (12 months after ingrowth core installation). This study provided new insights into the dynamics and traits of fine roots and will help to improve the accuracy of C flow estimation in hemiboreal forests with nutrient-rich organic soils in Latvia.
Flash Point Improvement of Mineral Oil Utilizing Nanoparticles to Reduce Fire Risk in Power Transformers: A Review
Authors: Khoirudin  , Kristiawan Budi , Sukarman  , Abdulah Amri , Santoso Budi , Wijayanta Agung Tri , Aziz Muhammad
Published: August 2024

Transformers are crucial equipment in electrical distribution systems but have a significant potential for failure. Insulation materials, including transformer oil (TO), play a primary role in transformer failures. A fire involving the TO can lead to a large explosion, causing the main tank to rupture and resulting in extensive damage to the entire transformer and the surrounding area. Mineral oil (MO) is the most widely used type due to its availability and relatively low cost compared to other types of oil. However, MO has a critical disadvantage, which is its very low flash point. The low flash point makes MO highly flammable. When the oil fires in an enclosed space, such as a transformer tank, the pressure inside the tank increases, leading to a large explosion. Therefore, research on increasing the flash point of MO is highly necessary. The application of nanotechnology is a promising approach to increasing the flash point of base fluids. Research on the effect of nanoparticles (NPs) on flash points is very limited in the literature; thus, there is significant potential for further research in this field. The majority of studies indicate an increase in flash points with the addition of NPs to MO. There is only one study that shows a decrease in flash point, which is −1.33% compared to MO. From all the reviewed studies, it can be concluded that NPs are a potential solution to increase the flash point of MO. Despite their benefits, NPs require a thorough examination of health and environmental impacts, along with proper waste management, to ensure their advantages.
The Anti-Diabetic Effect of Non-Starch Polysaccharides Extracted from Wheat Beer on Diet/STZ-Induced Diabetic Mice
Authors: Oumeddour Dounya Zad , Lin Wen , Lian Chang , Zhao Lei , Wang Xinyi , Zhao Liang , Guo Liyun
Published: August 2024

Diabetes mellitus (DM), a major cause of mortality, is characterized by insulin resistance and β-cell dysfunction. The increasing prevalence of DM is linked to lifestyle changes and there is a need for alternative approaches to conventional oral hypoglycemic agents. Polysaccharides, particularly non-starch polysaccharides (NSPs), have been identified as promising hypoglycemic agents. Cereals, especially wheat, are key sources of dietary polysaccharides, with NSPs derived from wheat beer attracting significant interest. This study aimed to investigate the hypoglycemic and hypolipidemic effects of NSPs extracted from wheat beer in STZ-induced diabetic C57BL/6J male mice. The results showed that NSPs extract positively influenced blood glucose regulation, lipid profiles, and liver and kidney functions, by attenuating liver AST and kidney CRE levels in a dose-dependent manner. The NSPs demonstrated anti-oxidative and anti-inflammatory properties, potentially providing significant benefits in managing diabetes and its complications. Moreover, the study revealed the histoprotective effects of NSPs on the liver and pancreas, reducing lipid deposition, necrosis, and inflammation. These findings highlight the multifaceted advantages of NSPs and suggest their potential as effective agents in diabetes management. This study supports the need for further research into the therapeutic potential of NSPs and their application in developing innovative treatments for diabetes and its associated complications.
Pumpkin and Pumpkin By-Products: A Comprehensive Overview of Phytochemicals, Extraction, Health Benefits, and Food Applications
Authors: Gavril (Rațu) Roxana Nicoleta , Stoica Florina , Lipșa Florin Daniel , Constantin Oana Emilia , Stănciuc Nicoleta , Aprodu Iuliana , Râpeanu Gabriela
Published: August 2024

A versatile and popular Cucurbitaceous vegetable, pumpkin has recently gained much attention because of its variety of phytochemicals and health advantages. Pumpkins are a type of winter squash, traditionally with large, spherical, orange fruits and a highly nutrient food. Pumpkin by-products comprise various parts, such as seeds, peels, and pulp residues, with their bioactive composition and many potential benefits poorly explored by the food industry. Pumpkin and their by-products contain a wide range of phytochemicals, including carotenoids, polyphenols, tocopherols, vitamins, minerals, and dietary fibers. These compounds in pumpkin by-products exhibit antioxidant, anticancer, anti-inflammatory, anti-diabetic, and antimicrobial properties and could reduce the risk of chronic diseases. This comprehensive review aims to provide a detailed overview of the phytochemicals found in pumpkin and its by-products, along with their extraction methods, health benefits, and diverse food and industrial applications. This information can offer valuable insights for food scientists seeking to reevaluate pumpkin’s potential as a functional ingredient. Reusing these by-products would support integrating a circular economy approach by boosting the market presence of valuable and sustainable products that improve health while lowering food waste.