bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of intravenous infusion of dexmedetomidine and magnesium during tympanoplasty
Authors: Shariatpanahi Elnaz , khajavi Mohamad , Nikooseresht Mahshid , Seifrabiei Mohamadali , Emami Seyede Faranak
Published: October 2024

Background
Increased bleeding volume during tympanoplasty (BDT), in addition to disturbing the surgeon's vision, increases the need for transfusion of blood products and the risk of a hemolytic reaction. This study aimed to compare the effect of intravenous infusion of dexmedetomidine (D) and magnesium sulfate (MS) on BDT.
Methodology:
In this clinical trial, 40 candidates for tympanoplasty were selected. They were randomly divided into two groups of 20, which included intravenous infusion of D at a dose of 1 µg/kg and MS at a dose of 40 mg/kg. The two groups were compared in terms of BDT, surgeon satisfaction level, surgery time, amount of drug use, and complications. Data were analyzed using SPSS26 (P < 0.05).
Results
In the D and MS groups, the mean ± standard deviations of age were 44.05 ± 14.10 and 43.65 ± 12.71. The findings showed that the differences in the mean of the variables were not significant (P > 0.05); BDT = 52.75 ± 15.52, 49.50 ± 15.21 CC; Surgeon satisfaction level = 30%, 35% (very good) and 20%, 50% (good); Surgery time = 116.25 ± 25.02, 109.00 ± 17.82 minutes; Narcotic used = 6.25 ± 1.37, 6.00 ± 1.57 CC; Frequency of complications (bradycardia, dry mouth, nausea) = 55%, 65%; Mean arterial pressure = 80.85 ± 13.36, 76.50 ± 7.09 ml; Mean heart rate = 75.85 ± 9.68, 77.00 ± 5.71 per minute; Anesthesia termination time = 15.26 ± 4.85, 15.40 ± 5.03 minutes; Success rate of surgery = 0.95%, 0.85%.
Conclusion
D and MS seem to have relatively similar effects in tympanoplasty. But since fewer complications were observed in patients receiving D. We suggest that D infusion is preferred in tympanoplasty.
Microscale metal additive manufacturing by solid-state impact bonding of shaped thin films
Authors: Reiser Alain , Schuh Christopher
Published: October 2024

The deposition of device-grade inorganic materials is one key challenge towards the implementation of additive manufacturing in microfabrication, and to that end, a broad range of physico-chemical principles has been explored for 3D fabrication with micro- and nanoscale resolution. Yet, for metals, we still lack a process that achieves material quality rivaling that of established thin-film deposition methods and at the same time has potential to combine high throughput production with a broad palette of processable materials. Here we introduce the kinetic, solid-state bonding of metal thin films for the additive assembly of high-purity, high-density metals with micrometer-scale precision. Indirect laser ablation accelerates micrometer-thick gold films to hundreds of meters per second without their heating or ablation. Their subsequent impact on the substrate above a critical velocity forms a permanent, metallic bond in the solid state. Stacked layers are of high density (>99%). By defining thin-film layers with established lithographical methods prior to launch, we demonstrate a variable feature size (2-50 μm), arbitrary shape of bonded layers and parallel transfer of up to 36 independent film units in a single shot. We thus establish the solid-state kinetic bonding principle as a viable and potentially versatile route for micro-scale AM of metals.
Spatially Confined Liquid-Liquid Phase Separation of Vegetable Protein Condensates Toward Commercializable High-protein Dysphagia Diets
Authors: Wang Tao , Wu Xin , Li Xuyuan , Xiong Wenfei , Wu Chao , Na Xiaokang , Liu Fei , Zhang Hao , Wu Weijie , Feng Wei , Wang Ren , Chen Xianfu , Binks Bernard
Published: October 2024

Global population aging has aroused a huge demand for high-protein dysphagia diets in the form of thickened fluids. However, customizing the colloidal stability and fluid mechanics of highly concentrated protein dispersions remains a challenging task, especially for naturally ubiquitous vegetable proteins. Here we put forward spatially confined liquid-liquid phase separation (SCLLPS) of proteins through a volume exclusion effect under the stipulation of polysaccharide crowders. Using SCLLPS, concentrated proteins (15.0 wt.%) partition from bulk solution and electrostatically configure at the negatively charged sites of polysaccharide networks, thus yielding spatial ordering of protein condensates and circumventing self-association. Such SCLLPS enables favorable polymer properties for manipulating colloidal stability and fluid mechanics pivotal to dysphagia diets. This approach is versatile and is validated by several plant and fungi proteins as well as alternative polysaccharides. Our strategy would deepen the utilization of agricultural proteins to combat geriatric syndromes and food shortages in the future.
An integrated method to evaluate the design of elderly-friendly home nursing beds
Authors: Liu Dong , Li Hui , Shi Yu
Published: October 2024

This research introduces an integrated evaluation model: TM-SEM-TOPSIS, to address the subjectivity and one-sidedness in indicator derivation, weight calculation, and scheme ranking in the design evaluation process. First, text mining (TM) and interviews were employed to select the evaluation indicators for elderly-friendly home nursing beds. Second, Structural Equation Modeling (SEM) was adopted to establish a model to gauge user satisfaction, and significant evaluation indicators were extracted using principal component analysis. AMOS was applied to analyze the model’s goodness of fit and how it works, elucidating the coefficients of evaluation indicators. Lastly, our research adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compute the Euclidean distances and the relative fit of the three nursing beds and make a ranked evaluation. The product evaluation system, design strategies, and comparative method discussed in our research can offer a vital reference for selecting or developing elderly-friendly products.
Structural Repetition Detector: multi-scale quantitative mapping of molecular complexes through microscopy
Authors: Henriques Ricardo , Mendes Afonso , Saraiva Bruno , Jacquemet Guillaume , Mamede João , Leterrier Christophe
Published: October 2024

From molecules to organelles, cells exhibit recurring structural motifs across multiple scales. Understanding these structures provides insights into their functional roles. While super-resolution microscopy can visualise such patterns, manual detection in large datasets is challenging and biased. We present the Structural Repetition Detector (SReD), an unsupervised computational framework that identifies repetitive biological structures by exploiting local texture repetition. SReD formulates structure detection as a similarity-matching problem between local image regions. It detects recurring patterns without prior knowledge or constraints on the imaging modality. We demonstrate SReD's capabilities on various fluorescence microscopy images. Quantitative analyses of three datasets highlight SReD's utility: estimating the periodicity of spectrin rings in neurons, detecting HIV-1 viral assembly, and evaluating microtubule dynamics modulated by EB3. Our open-source ImageJ and Fiji plugin enables unbiased analysis of repetitive structures across imaging modalities in diverse biological contexts.
Characterization and stability of soy protein-calcium lactate emulsion stabilized by sodium tripolyphosphate
Authors: Jiang Zuqiang , Li Yonghong , Ying Ruifeng , Huang Meigui , Liao Qiuhong , Hussain Shahzad , Hayat Khizar
Published: October 2024

Soy protein-calcium lactate emulsion stabilized by sodium tripoly-phosphate is of great significance for the development of calcium-fortified soymilk products. This study aimed to investigate the characteristics and stabilities of soy protein-calcium lactate emulsions stabilized by sodium tripolyphosphate. A soy protein-calcium lactate emulsion stabilized with sodium tripolyphosphate of 0.6%(w/v) served as the best balance between low concentration and high stability. At 0 to 0.6% sodium tripolyphosphate, the emulsion potential, emulsifying activity index, emulsifying stability index, and interfacial protein adsorption rate gradually increased with the increase in sodium tripolyphosphate concentration. When the concentration was 0.6%, the absolute potential reached 35.5 mV, the emulsifying activity index reached 30.9 m2/g, and the emulsifying stability index reached 142.1 min. The interfacial protein adsorption rate was 71.7±0.8%. However the particle size, chromatography index, and flocculation index decreased and were 756.2±41.3 nm, 2.8±0.6% and 16.21%, respectively, at a 0.6% concentration. And when the concentration exceeded 0.6%, the above characterization data gradually stabilized. From the perspective of microstructure, the droplet distribution was uniform, and the centrifugal stability, temperature stability, and storage stability of the emulsion were enhanced. Raman spectra showed that the secondary structure of the protein in the emulsion changed, and the stability of the emulsion increased with the increase of ordered structure. These findings provided insights into the properties of soy protein-calcium lactate emulsions stabilized by sodium tripolyphosphate and might help expand their use in emulsions and soy products.
Study on the persistent and efficient wire electrical discharge machining of SiCp/Al
Authors: Yang Hailin , Liu Zhidong , Wang Xuan , Wu Xiaochuan
Published: October 2024

The high-speed wire electrical discharge machining (HS-WEDM) of high-volume-fraction SiC particle-reinforced aluminum matrix composites (SiCp/Al) can only occur under low-energy conditions. Wire breakage easily occurs during the cutting of SiCp/Al through the conductive block power supply method under high-energy conditions, and the causes of this problem are analyzed in this study. To address this problem, an open-guide-wheel power supply method is proposed. This method increases the contact area and angle between the wire electrode and the guide wheel, achieving a stable power supply through rolling contact and effectively suppressing the electric corrosion effect. Additionally, the open structure resolves the issue of the power supply device burning out, which occurs with the traditional closed structure. The experimental results show that using the open-guide-wheel power supply method achieves persistent and efficient cutting of 70 mm 55vol% SiCp/Al. The cutting efficiency is 111.65 mm2/min, which is more than three times the long-term cutting efficiency of the conductive block power supply method.
Design of Quasi-Optical Mode Converter for High-Power 170 GHz TE 17,6 Mode Gyrotrons
Authors: Sharif Hamid , Jamil Muhammmad Haris , He Wenlong , Abbas Nazish Saleem
Published: October 2024

A quasi-optical mode converter for a high-performance gyrotron operating in TE17,6 mode at 170 GHz has been developed. This converter features a dimpled-wall launcher and employs a two-stage perturbation method based on coupled mode theory, complemented by a specialized mirror system. MATLAB code was developed to analyze the electromagnetic field in the launcher wall and mirror configuration. The radiation field emitted by the launcher is evaluated in free space, and the mirror system consists of a quasi-elliptical mirror, an elliptical mirror, and a parabolic mirror to ensure effective phase correction. The simulation results show that after phase correction, the scalar Gaussian mode content and vector Gaussian mode content of the output window reach 98.3% and 96.0%, respectively.
Effects of Integrated Selected Yoga Practices on Physical and Mental Health Among Destitute Elderly Widows in Vrindavan: a Pilot Study
Authors: Varshney Jyoti , Penna Madhusudan
Published: October 2024

Background
Vrindavan has gained a reputation for harbouring many homeless elderly widows who confront tremendous physical, emotional, and financial hardships. These difficulties emphasize larger issues, such as poverty, social isolation, and poor healthcare for India's elderly. Yoga has recently gained recognition for its capacity to improve the health and well-being of these widows.
Purpose
This study aimed to test the effects of 4 weeks Integrated Selected Yoga Practices in improving Body pain, and decreasing Fatigue, Hypertension, Heart Rate, Stress, Anxiety and Depression in destitute elderly widows.
Methods
A quasi-experimental pre-test and post-test with a control group design was used. A convenience sample of 20 destitute elderly widows was divided into an Intervention Group (IG) (n = 10) and a control group (CG) (n = 10). The outcome measured of NRS, FAS, BP & HR by Digital Monitor, GAS, GDS and PSS were examined at baseline and the 4th week of the study.
Results
The Intervention Group (IG) showed a significant reduction in Body pain, Fatigue, Blood Pressure, Heart Rate, Anxiety, Depression and stress after 4 weeks compared to baseline.
Conclusion
It is recommended that Integrated Selected Yoga Practices be incorporated as a daily activity program in shelter homes or other long-term care facilities to improve Body pain and decrease Stress in destitute elderly widows.
The Effect of Sleep Duration on Obesity and Academic achievement of Medical Students
Authors: ALzaim Anas , Mohamad Malek , Hammoud Taghrid
Published: October 2024

Objective: To study the effect of the number of hours of sleep on body mass index and academic achievement among students of the Faculty of Human Medicine in two private Syrian universities.
Methods and materials: The current cross-sectional study was conducted during the period from 1/8/2024 to 1/10/2024 on 346 participants, 191 males and 155 females. The participants were students in the Faculty of Medicine from the first, second, third, fourth, fifth and sixth academic years.
Results: The average body mass index of students was 23.22 ± 4.03 kg/m2, the percentage of overweight, normal weight, and underweight (26.9%, 63.3%, and 9.8% respectively), the average number of sleeping hours of students was 7.82 ± 1.97 hours, the percentage of those with a very good, good, and acceptable university grade (33.2%, 65.9%, and 0.9% respectively), there was no statistically significant difference in the average number of sleeping hours according to the body mass index and according to the university grade.
Conclusion: It is important to conduct more of these studies in order to spread awareness about the importance of not excessive sleep hours among students.
Determining the efficient mass ratio of the extractant and the object of extraction and the equilibrium concentrations of the target components in them
Authors: Arabova Z. M. , Aleksanyan I. Yu. , Nugmanov A. H.Н. , Konnova O. I. , Aleksanyan A. I. , Yasnov A. S.
Published: October 2024

The caviar and lecithin complex of freshwater fish can be widely used for the production of various food products, including those of functional orientation. The main technological operations in lecithin technology should include the processes of extraction of the initial caviar raw material and raffinate obtained after the first stage of selective extraction. The purpose of the study is to identify the rational mass ratio of extractant and raw material (module), on which the extraction efficiency depends, as well as the final concentration of extracted substances in the extract to determine its current value in the process of mass transfer to study the kinetics of extraction. The object of the study is the caviar-anchovy complex of fathead carp, carp, pikeperch and catfish, as well as the extract and raffinate from it. Summarizing the experimental data on revealing the rational module, as well as the final concentration of extracted substances in the extracts obtained, we can conclude that for caviar of the fish species under study, the rational hydromodule for its extraction with acetone is the ratio of dry caviar : extractant 1 : 4, and for its extraction with ethyl alcohol is the ratio of dry caviar : extractant 1 : 8. If the mass ratio refers to the fresh caviar of the studied fish species, then its rational indicator at its extraction with acetone is the ratio of fresh caviar : extractant 1 : 1.5, and at its extraction with ethyl alcohol is the ratio of fresh caviar : extractant 1 : 3. The average final concentration of extracted substances from dry caviar of the studied fish species in the extracts obtained, if acetone was used as a solvent under rational conditions of mass transfer, corresponds to the value of 1.3 %, and if ethyl alcohol was used as a solvent under rational conditions of mass transfer, corresponds to the value of 1.0 %.
The use of protein hydrolysate from fish waste as part of microbiological culture media
Authors: Zhivlyantseva Iu. V. , Kuranova L. K. , Grokhovsky V. A.
Published: October 2024

The need to involve in the processing of significant amount of secondary protein-containing raw materials formed during fish cutting, on the one hand, and on the other hand, the shortage of a source of the protein component of microbiological nutrient media determines the relevance of the work. The goal was to study the possibility of using fish protein hydrolyzate obtained by enzymatic hydrolysis from secondary fish raw materials in microbiological nutrient media. Microbiological and physicochemical methods were used to conduct research. When optimizing the developed algorithm for the fermentation process, a mathematical model of the experiment was used with a minimum number of experiments. An algorithm has been developed for obtaining fish protein hydrolyzate from meat and bone waste from cutting pelagic fish (cod). The hydrolysis parameters were optimized: the concentration of the enzyme preparation is 1.33 % of the total mass of waste, the duration of the fermentation process – 3 hours. The characteristics of the resulting fish hydrolyzate are determined: the mass fraction of total nitrogen – 13 %, amine – 3.6 %, water – 4.6 %, sodium chloride – 2.7 %, fat – 0.3 %. An experimental fish hydrolyzate has been studied as a source of protein nitrogen in accordance with the formulation of a microbiological medium used for counting when growing colonies of microorganisms. The effectiveness of the prepared nutrient media was assessed by comparing the performance coefficients of the experimental and control nutrient media, as well as by identifying test microorganisms grown on the experimental medium. It was established that the growth pattern of test cultures both on the studied nutrient medium and on the control medium was almost the same. Test microorganisms retained their biochemical, morphological and cultural characteristics. The research results showed the possibility of using fish protein hydrolyzate obtained by enzymatic hydrolysis from secondary fish raw materials in microbiological nutrient media.
The effect of functional ingredients on the quality of the curd product and its storage capacity
Authors: Pakusina A. P. , Zakharova L. M. , Pashina L. L. , Babukhadiya K. R. , Shkolnikov P. N.
Published: October 2024

The solution to the problem of rationalizing the nutrition of the population, reducing the negative impact on the human body of harmful factors (physical, chemical) of the external environment is possible by creating functional products that include proteins, vitamins, antioxidants, pectins and other essential nutrients, the deficiency of which has been noted by biomedical research. Based on theoretical and experimental studies, the components were selected including functional ingredients: zosterin – a natural polysaccharide and vegetable filler – honeysuckle berry juice. Low-fat cottage cheese, which includes proteins, minerals, sulfur-containing compounds and other substances, was chosen as the basis of the functional product. As a result of the conducted research, technological approaches were formed and the component composition of the curd product with zosterin and berry juice was justified. The ratios of zosterol and honeysuckle juice in relation to the mass of cottage cheese have been established, ensuring proper technological and preventive effect. The organoleptic, structural-mechanical, physico-chemical parameters of the curd product have been studied. The nutritional and energy value is calculated. The developed curd product has an increased nutritional value, it contains biologically important components – vitamins, mineral salts, polyphenolic substances, pectins. It has a high digestibility due to the homogenization of all components and complementary organoleptics: pleasant aroma and taste, homogeneous, delicate consistency, attractive external characteristics. The effect of zosterol and berry juice on the quality of the curd product during storage has been studied. It has been proven that the introduction of zosterol and berry juice allows you to slow down undesirable processes, and, consequently, extend the shelf life of the product.
Fisher zeroes and the fluctuations of the spectral form factor of chaotic systems
Authors: Bunin Guy , Foini Laura , Kurchan Jorge
Published: October 2024

The spectral form factor of quantum chaotic systems has the familiar ‘ramp ++ plateau’ form. Techniques to determine its form in the semiclassical or the thermodynamic limit have been devised, in both cases based on the average over an energy range or an ensemble of systems. For a single instance, fluctuations are large, do not go away in the limit, and depend on the element of the ensemble itself, thus seeming to question the whole procedure. Considered as the modulus of a partition function in complex inverse temperature \beta_R+i\beta_IβR+iβI (\beta_I \equiv \tauβI≡τ the time), the spectral form factor has regions of Fisher zeroes, the analogue of Yang-Lee zeroes for the complex temperature plane. The large spikes in the spectral form factor are in fact a consequence of near-misses of the line parametrized by \beta_IβI to these zeroes. The largest spikes are indeed extensive and extremely sensitive to details, but we show that they are both exponentially rare and exponentially thin. Motivated by this, and inspired by the work of Derrida on the Random Energy Model, we study here a modified model of random energy levels in which we introduce level repulsion. We also check that the mechanism giving rise to spikes is the same in the SYK model.
Critical buckling load analysis of Euler-Bernoulli beam on two-parameter foundations using Galerkin method
Authors: Ike Charles Chinwuba
Published: October 2024

The critical buckling load determination of Euler-Bernoulli beams on two-parameter elastic foundations (EBBo2PFs) is important to avert buckling failures. The governing equation for buckling of thin beam on two-parameter elastic foundation is a homogeneous ordinary differential equation (HODE) of fourth order and constant parameters when the beam is prismatic and homogeneous. The HODE has been solved in this work by Galerkin method for simply supported, clamped and clamped-simply supported ends. One-parameter algebraic shape function formulation was used to reduce the problem to an algebraic eigenvalue problem, which is solved to find the critical buckling load for each studied case. The critical buckling load for EBBo2PF for simply supported boundary conditions was found to be closely identical to the exact solutions. The solutions for clamped-clamped edges and clamped-simple supports were found to be accurate. The merit of the Galerkin method is the simplicity and the accuracy even when one-parameter shape function has been used.
What is a γρυπαίετος (Aesch. Fr. inc. fab. 422 R. )?
Authors:   Universidad de Extremadura, Librán Moreno Míriam
Published: October 2024

The purpose of the following paper is to identify what kind of mythical creature Aeschylus’ γρυπαίετος (‘griffin–eagle’) that caused such a scandal for ‘Euripides’ in Aristophanes’ Frogs 928–930 (= Aesch. fr. inc. fab. 422 R.) was. The term has usually been interpreted in three ways: (a) as a poetic form of ‘eagle’; (b) as a poetic form of ‘griffin’; (c) as ‘eagle of the griffin species’. The testimony of Aristophanes’ Frogs and vase-painting suggests that it may have been an idiosyncratic, archaic type of griffin, called by modern specialists ‘griffin-bird’ and characterised by having two legs, not four, and the body of a bird, not a lion. This fantastic creature appeared quite frequently on Archaic black-figure vases in Athens, but had completely disappeared by the end of the 6th century BC. As a result, its appearance would be unknown to Aristophanes’ public, making the term γρυπαίετος impossible to make out (Ra. 930). Thus, the following paper suggests that Aeschylus’ γρυπαίετος (‘griffin-eagle’) is a fabulous composite beast made up of griffin and eagle parts, as its name suggests: a griffin head (with an open hooked beak, long pointed ears, a protuberance or horn over the eyes, and a curl or plume falling down one side of the neck) crowning an eagle body (two-legged, feathered, with wings and talons).
Research on a cloud model intelligent computing platform for water resource management
Authors: Wang Tao , Duan Jingjing , Zhai Jiaqi , Zhao Jing , Gao Yulong , Gao Feng , Zhang Longlong , Zhao Yifei
Published: October 2024

As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references for intelligent water management and refined allocation of water resources in the Weihe River Basin.
Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020
Authors: Chen Sheng-Jin , Tang Bo-Hui , Ma Xianguang , He Zhi-Wei , Fu Wei , Chen Junyi
Published: October 2024

Evapotranspiration (ET) is vital for the Earth's energy and water balance, particularly influenced by global climate change. The Yunnan-Guizhou Plateau (YGP), characterized by abundant water resources and intricate terrain, has been a subject of study. However, previous research often overlooked intra-annual climate variations in ET. This study employed high-spatiotemporal-resolution ET data from 2003 to 2020 to quantitatively analyze the spatiotemporal characteristics of ET on the YGP. The annual ET showed an increasing trend of 0.18 mm/year, with monthly ET increases in January, March, November, and December, mainly influenced by vegetation transpiration, which accounts for 56% of ET. Breakpoints in ET trends and seasonal components occurred in January 2007 and June 2018. The geodetector model assessed the impact of 15 driving factors on ET, with net radiation and vegetation index playing dominant roles with q-values of 0.29 and 0.24. Factor impacts varied seasonally, with greater influence in the dry season (q-value of 0.53 for net radiation in January) and less in the rainy season (q-value of 0.08 in August). Pearson correlation analysis indicated that different driving factors influenced ET in different months. These findings enhance understanding of plateau ET responses to climate-change mechanisms.
Climate change impacts on maternal health and pregnancy outcomes in Africa
Authors: Dumbuya Salifu , Chabinga Rhodah , Ferede Manaye Asefa , Saber Mohamed
Published: October 2024

The review examines how climate change adversely affects maternal health and pregnancy outcomes in Africa, a region particularly vulnerable to climate-related disasters. It highlights the increased incidence of tropical and waterborne illnesses due to climate change, disproportionately impacting expectant mothers. The study thoroughly evaluates the effects of extreme weather events like heatwaves and floods on maternal health, both directly and indirectly. It underscores significant gaps in policy and research within African health sectors regarding these issues. Key findings reveal that maternal death rates remain alarmingly high, with risks like preterm birth, stillbirth, and maternal hypertension exacerbated by climate change. The review calls for urgent action, including enhanced research, increased funding for climate adaptation, and the integration of maternal health into broader climate resilience strategies. Additionally, it emphasizes the need for greater awareness and international collaboration to strengthen health systems in Africa, particularly addressing the vulnerabilities of pregnant women. This work aims to enhance understanding among policymakers and researchers about the critical health impacts of climate change on pregnant women in Africa.
Modelo de tomada de decisão suportado pela integração de valores e fluxos informacionais
Authors: Silva Rafael Felix da , Razzolini Filho Edelvino
Published: October 2024

Este artigo objetivou modelar o processo de tomada de decisões não programadas, em nível estratégico, de organizações sustentáveis, evidenciando a integração entre valores e fluxos de informação no suporte ao processo decisório. Para tanto, empreendeu-se uma pesquisa descritiva, que recorreu à aplicação de questionários semiestruturados a tomadores de decisão de empresas listadas no Índice de Sustentabilidade Empresarial da Brasil Bolsa Balcão, no ano de 2023. Os resultados da pesquisa destacam a integração entre valores e fluxos informacionais, enfatizando a relevância desta integração no suporte à tomada de decisão em organizações sustentáveis.  A partir destes resultados se delineou um modelo decisório em sustentabilidade, que além de contemplar critérios ou dimensões considerados relevantes para questões da sustentabilidade empresarial, também enfocou os fluxos de informações que apoiam o processo decisório, por meio dos valores que influenciam seu modo de ocorrência, permitindo a identificação de etapas decisórias, e o mapeamento de procedimentos realizados ao longo da tomada de decisão. Acredita-se que o modelo proposto pode contribuir para decisões mais convergentes entre objetivos e consequências, atenuando as dificuldades de agregação das dimensões de sustentabilidade às estratégias de negócio das organizações.
Advancements in Remote Heart Monitoring: Wearable Technology and AI-based Approaches for Cardiovascular Disease Detection
Authors: S. Immaculate Joy , S. Kanagamalliga
Published: October 2024

In the era of precision medicine and individualized approaches, remote
monitoring and control of heart function have emerged as critical components of
patient evaluation and management. The integration of consumer-grade software and
hardware devices for health monitoring has gained popularity as technological
advancements become increasingly integrated into daily life. The cardiology
community must adapt to the demands of distant and decentralized care, as highlighted
during the COVID-19 pandemic. Wearable technology, such as vital sign monitors,
holds significant potential for monitoring heart disease and associated risk factors. This
book chapter explores the expanding applications of wearable technology in
cardiology, focusing on examples such as Holter-event recording and
electrocardiogram (ECG) patches. Textile-based sensors and wristbands are
implemented across various patient groups, emphasizing real-time deployment and the
evolving role of wearables in arrhythmia, cardiovascular disorders, and associated risk
factors. The importance of conducting clinical trials and using proper terminology for
clinical validation is also highlighted. To enhance the accuracy and efficiency of ECG
signal analysis, this chapter proposes a novel approach that combines AI-based
unsupervised Long Short-Term Memory (LSTM) with a recursive-based Ensemble
Neural Network (ENN). The LSTM component effectively denoises raw ECG signals
and enables faster convergence. The ENN, with its built-in deep layers, provides an
improved classification of cardiovascular diseases (CVD) present in the input ECG
data. The recursive approach employed by the ENN efficiently utilizes the available
parameters, even in the presence of noisy labels. The proposed method demonstrates
enhanced prediction and classification of CVD, with high precision, recall, and F1
score. The objective is to derive a checkpoint between clinical and research potentials,
identify gaps, and address potential risks associated with CVD detection using ECG
measurements. By leveraging wearable technology and advanced AI techniques,
clinicians and researchers can benefit from improved diagnostic accuracy, remote patient monitoring, and personalized care. The insights gained from this chapter will
contribute to the ongoing advancements in remote heart monitoring and facilitate the
adoption of innovative approaches in cardiovascular disease management.
Neural Network Models for Feature Extraction and Empirical Thresholding
Authors: Dhawale Krupali
Published: October 2024

Neural Network Models for Feature Extraction and Empirical Thresholding
study the combination of neural network models and empirical thresholding methods to
improve the procedure for extracting features. For researchers and practitioners
working in the fields of feature extraction and machine learning, it illustrates the
advantages, approaches, and difficulties connected with this integration and offers
helpful insights. The basic concepts of feature extraction are covered in this book
chapter, along with an overview of the several neural network models that can be used
to accomplish this task, such as auto-encoders, convolutional neural networks (CNNs),
and recurrent neural networks (RNNs). This book chapter emphasizes the benefits,
methodologies, and challenges associated with this integration, providing valuable
insights for researchers and practitioners in the fields of feature extraction and machine
learning. This book chapter is useful for statistical analysis, domain expertise-driven
threshold selection, and validation metrics-based threshold choice as efficient
techniques for enhancing feature quality and lowering noise.
Comparing Different Machine Learning Techniques for Detecting Phishing Websites
Authors: Pathak Astha , Pandey Prashant , Raheja Vaishali
Published: October 2024

Phishing site URLs are designed to gather confidential data such as user
identities, passwords, and transactions involving online money. Phishing strategies
have begun to advance quickly as technology advances; this could be avoided by using
anti-phishing tools to identify phishing. Employing machine learning techniques to
identify fraudulent websites was previously suggested and put into practice. This
project's primary goal is to develop the system in a way that is highly efficient,
accurate, and economical. Delivered to the system, the dataset of genuine and phishing
URLs is pre-processed to put the data in a format that can be used for analysis. Each
category has unique, defined phishing features against a dataset of real and fake URLs.
We evaluated the classifier's performance using a different test set after training it and
its values. A classifier has been created for phishing websites and tested for
effectiveness with a set of labeled phishing and legal URLs. When compared to seven
different classifiers of machine learning, the proposed model scored the greatest test
accuracy of up to 97.5% with the Gradient Boosting Classifier.
The Future of Computer-Aided Drug Discovery Methods: Advancements and Opportunities
Authors: Vlasiou Manos C.
Published: October 2024

The future of Computer-Aided Drug Discovery (CADD) methods is
characterized by transformative innovations. Artificial intelligence and machine
learning are enhancing accuracy in predicting drug-target interactions and
pharmacokinetics, with deep learning models leading in performance. Quantum
computing is poised to revolutionize molecular simulations. Big data and omics
integration enable precision medicine, tailoring treatments to individual patient
profiles. Cloud-based platforms democratize CADD tools and promote global
collaboration. Ethical considerations, data privacy, and regulatory challenges are
gaining prominence. With robust ethical guidelines and regulatory frameworks, the
future of CADD promises safer and more efficient drug discovery, ensuring that novel
therapies meet the diverse needs of patients worldwide.
Medical Imaging Using Machine Learning and Deep Learning: A Survey
Authors: Sharma Uma , Sharma Deeksha , Pathak Pooja , Singh Sanjay Kumar , Singh Pushpanjali
Published: October 2024

Machine learning and deep learning which are the subsets of Artificial
intelligence, have numerous uses in medical imaging. Advancements in machine
learning and deep learning led to drastic improvements in medical imaging fields like
the evaluation of risks, recognition, identification, prediction, and treatment results.
The decision-making power of computers based on artificial intelligence has elevated
the effectiveness and efficiency of human decisions. Techniques based on machine
learning and deep learning are not only effective and efficient but also speedy. In the
medical field, the stage of the diagnosed disease is of great importance as the treatment
and recovery rates depend on it. So based on the best and fastest decisions given by
machine learning and deep learning techniques, medical practitioners can give their
services in a better way.
We have given a summary of the methods used in medical imaging based on machine
learning and deep learning algorithms with the benefits and pitfalls of these algorithms.
These algorithms offer remarkable methods for classification, segmentation, and
autonomous decision-making ability for the analysis of medical images.
Healthcare Machine Learning Insights
Authors: Kumar Ajay , Singhal Kanika , Kushwah Kirti
Published: October 2024

Machine learning can potentially improve the medical industry by providing
different healthcare opportunities. Medical records that previously required human
intervention can now be processed using a machine-learning algorithm in seconds. It
can learn like humans and adjust to new inputs in a very efficient way. The quality of
treatment has also improved. The correct diagnosis of disease and analysis of additional
data on a patient’s condition using machine learning is helping doctors to make the
process simple and efficient. Doctors can simplify and expedite the process with the aid
of machine learning, which facilitates accurate disease diagnosis and extra data
analysis regarding a patient's condition. Machine learning algorithms also help in
discovering unexpected patterns in clinical trials. But things are not as simple as they
seem to be. Opportunities are always paired with challenges. The results we get from
machine learning algorithms depend on the quality of data we feed into it and there is
no guarantee of the fact that medical data is always precise and accurate. There may be
gaps in records and it may be inaccurate. Lack of quality data to build precise
algorithms can be a major challenge. In this chapter, we will be presenting the
opportunities provided by machine learning in healthcare and also the challenges that
are making things difficult.
A Scientific Implementation for Medical Images to Detect and Classify Various Diseases Using Machine Learning
Authors: Goel Rati , Mishra Suchismita
Published: October 2024

Reconstruction of medical images is imperative for the comprehension of
clinical anomalies. Various processes and techniques are employed to generate efficient
anatomical representations of the human body in medical imaging. This technique
provides physicians with a visual depiction of internal organs, aiding in the verification
of ongoing treatments, accurate diagnoses, and treatment planning. Medical imaging
encompasses diverse methods such as ultrasound, X-rays, MRI, and CT scans, with the
choice depending on the condition of the ailment, such as kidney stone diseases, breast
cancer, and brain tumors. However, the quality of medical images can becompromised
due to different sources of noise and blurriness. This chapter introduces an advanced
image processing methodology to detect diseases in medical images, particularly brain
tumors, kidney stones, and breast cancer using ultrasound and MRI images. The
proposed approach involves converting RGB medical images into grayscale, removing
labels, and adjusting image intensity to enhance the contrast of biomedical images.
Median filtering is applied to eliminate noise, and the Discrete Wavelet Transform
(DWT) is utilized for brain tumor detection. The filtered medical image output is
subjected to morphological and k-means clustering segmentation. To classify the
images into two categories benign and malignant, Convolutional Neural Network
(CNN) classifiers are employed. The final system analysis involves evaluating,
specificity, accuracy, and sensitivity through the preparation of a confusion matrix. The
classification system demonstrates an accuracy of approximately 95%. This presented
technique holds potential in supporting doctors with early detection for precise patient
treatment.
Zoveel mogelijk niets doen
Authors: Blanker Marco
Published: October 2024

Huisartsen worden geconfronteerd met steeds complexere patiëntvragen, systeembeperkingen, en de druk om onnodig veel administratieve taken uit te voeren, zegt Marco Blanker in zijn oratie. Hij benadrukt het belang van plezier en efficiëntie in de huisartsgeneeskunde, geïnspireerd door huisartsen die hij vroeg in zijn carrière ontmoette. Het persoonlijke contact en de langdurige relatie tussen huisarts en patiënt zijn cruciaal voor goede zorg, waarbij luisteren en duiden centraal staan. Blanker schetst de uitdagingen van de toegankelijkheid van zorg in Nederland, waaronder vergrijzing, een toename van chronische ziekten, complexiteit van het zorgstelsel, en het verlies van vertrouwen tussen zorgverzekeraars en zorgverleners. Om deze problemen te adresseren, pleit Blanker voor een herstel van vertrouwen in zorgverleners, passende financiering, betere communicatie over wat huisartsen wel en niet kunnen bieden, en de noodzaak voor huisartsen om "nee" te durven zeggen wanneer zorgvragen niet bij hen thuishoren. Blanker roept huisartsen en zichzelf op open te staan voor innovatie binnen de huisartsenzorg, zoals het integreren van nieuwe zorgvormen en technologieën, en het bevorderen van samenwerking met andere zorgprofessionals, zoals wijkverpleegkundigen. Deze samenwerking is volgens hem essentieel voor het toekomstbestendig maken van de eerstelijnszorg. Veranderingen in de zorg zijn noodzakelijk én mogelijk om de toegankelijkheid en kwaliteit van de Nederlandse gezondheidszorg te waarborgen. De vakgroep onderzoek huisartsgeneeskunde draagt actief bij aan deze ontwikkelingen. Blankers oproep tot actie, gericht aan zowel zorgverleners als beleidsmakers, onderstreept de urgentie van deze veranderingen om de zorg voor toekomstige generaties veilig te stellen.
Use of Digital Tools in Arbovirus Surveillance: Scoping Review
Authors: Melo Carolina Lopes , Mageste Larissa Rangel , Guaraldo Lusiele , Paula Daniela Polessa , Wakimoto Mayumi Duarte
Published: October 2024

Background

The development of technology and information systems has led to important changes in public health surveillance.

Objective

This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance.

Methods

The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society).

Results

Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance.

Conclusions

In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools’ data, considering ethical aspects.