bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Influencing Postoperative Sliding Distance in Elderly Patients with Intertrochanteric Fractures Treated with PFNA-II: A Retrospective Observational Study
Authors: Wu Dongwei , Guo Haichuan , Li Jingqiao , Li Chengsi , Wang Tianyu , Yang Zhenbang , Cheng Xinqun , Zhu Yanbin , Zhang Yingze , Zhang Shian
Published: October 2024

Purpose
Excessive sliding of intramedullary nails following surgical treatment of intertrochanteric fracture has been strongly associated with the development of mechanical complications and unfavorable results. This study aims to investigate the factors that influence the postoperative sliding distance in elderly patients treated with PFNA-II device for intertrochanteric fracture fixation.
Methods
A retrospective analysis of patients over 65 years old with intertrochanteric fractures using PFNA-II device in a university teaching hospital was performed between January 2020 and December 2021. Data on sociodemographics, comorbidities, operative procedure and postoperative radiographic sliding distance were collected. The Youden index was used to determine the threshold for excessive sliding, and the risk of mechanical complication in relation with excessive sliding was calculated using multivariate regression analysis. Also, potential factors influencing the slinding distance as a continuous outcome variable were investigated using univariate and multivariate linear regression analysis.
Result
Among the 507 eligible patients, the mean postoperative sliding distance was 4.45 mm (SD, 5.39 mm; range, 0-31.67 mm). The threshold for excessive sliding was determined as 6.75 mm, and 103 patients (20.3%) had excessive sliding, among whom 18 (17.5%) developed mechanical complications. The risk of postoperative mechanical complications was significantly higher in the excessive sliding group (adjusted OR = 14.714, 95% CI: 4.270–50.710, P < 0.001) than in non-excessive sliding group. Multiple linear regression analysis revealed that older age (B = 0.061, 95% CI: 0.007–0.114, P = 0.026), poor reduction quality (B = 4.736, 95% CI: 1.236–8.235, P = 0.008), and subtype P in lateral view (B = 11.193, 95% CI: 6.836–15.549, P < 0.001) were positively correlated with postoperative sliding distance.
Conclusions
Age, reduction quality and LAT reduction mode are important predictors of helical blade sliding distance after intertrochanteric fracture surgery. Surgeons should pay close attention to these factors during preoperative planning and postoperative rehabilitation protocols.
Heterogeneity in Treatment Response for Patent Ductus Arteriosus: A Meta-Analysis
Authors: Matsushita Felipe Yu , Krebs Vera Lúcia Jornada , de Carvalho Werther Brunow
Published: October 2024

Context:
While pharmacological interventions promote PDA closure, their impact on overall outcomes remains uncertain due to conflicting results. These inconsistent results indicate that the effectiveness of these treatments may vary considerable among preterm infants, suggesting potential heterogeneity.
Objective
This meta-analysis and meta-regression aimed to assess the effect of pharmacological interventions on mortality and PDA closure in preterm infants, while critically examining sources of heterogeneity.
Data Sources:
We searched Ovid MEDLINE and EMBASE for relevant studies.
Study Selection:
Studies comparing ibuprofen, acetaminophen, indomethacin, or placebo/expectant management in preterm infants with PDA, where the outcome of interest was either mortality or PDA closure.
Data Extraction:
We extracted data on mortality, PDA closure, study design, and patient baseline characteristics following PRISMA guidelines. We used a random-effects model to account for the heterogeneity observed in the studies.
Results
Meta-analysis of 72 RCTs revealed that while interventions significantly improved PDA closure rates (OR 5.31, p < 0.00001), they did not consistently reduce mortality (OR 1.03, p = 0.84). Notably, interventions appeared to increase mortality in infants with hemodynamically significant PDA (OR 1.45, p = 0.05). Our analysis revealed substantial heterogeneity (I2 = 55%) and significant inconsistencies in outcome reporting across studies. Meta-regression models could not fully explain the observed variability.
Limitations:
Potential publication bias, incomplete patient-level data, and inconsistent definitions across studies.
Conclusions
The substantial heterogeneity underscores the complexity of PDA and the limitations of a one-size-fits-all approach. These findings strongly support a shift towards precision medicine in PDA treatment, focusing on identifying factors that predict individual response.
Potential predictive value of CD8A and PGF protein expression in gastric cancer patients treated with neoadjuvant immunotherapy
Authors: Zhang Chengjuan , Wang Tingjie , Yuan jing , Xu benling , Bai Ruihua , Tang Xiance , Zhang Xiaojie , Wu Minqing , Lei Tianqi , Xu Wenhao , Guo Yongjun , Li Ning
Published: October 2024

Background
Immunoneoadjuvant therapy has garnered considerable attention owing to significant strides in cancer treatment. We aimed to explore the molecular mechanisms underpinning immunoneoadjuvant therapy through a comprehensive multiomics analysis using samples from a registered clinical trial cohort.
Methods
Preoperative samples were collected from 16 patients, and postoperative samples were obtained from 12 among them. RNA-seq and Olink proteomics were employed to identify key genes before and after neoadjuvant treatment. The weighted coexpression network was constructed using Weighted gene co-expression network analysis (WGCNA). Furthermore, the proportion of infiltrated immune cells was calculated using xCell based on normalized expression data derived from RNA-seq.
Results
Patients were stratified into T1 (good efficacy) and T2 (poor efficacy) groups based on Tumor Regression Grade (TRG) to neoadjuvant immunotherapy. Compared to the T2 group (TRG2 and TRG3), the T1 group (TRG0 and TRG1) showed significant differences in pathways related to inflammatory response and myeloid leukocyte activation. Furthermore, the T1 group exhibited elevated levels of CD8 + T cells and B cells. The top two factors with the highest area under the Receiver Operating Characteristic (ROC) curve were CD8a molecule (CD8A) (1.000) and C-C motif chemokine ligand 20 (CCL20) (0.967). Additionally, the expression of Placenta Growth Factor (PGF) and TNF receptor superfamily member 21 (TNFRSF21) proteins significantly increased compared to the T2 group. High expression of CD8A and PGF were associated with favorable and poor prognosis in gastric cancer patients, respectively. Immunoinfiltration analysis revealed a positive correlation between CD8A and Dendritic Cell (DC) levels, while a negative correlation was observed with Myeloid-derived suppressor cell (MDSC) levels.
Conclusions
Through multiomics analysis, we discovered that CD8A is linked to enhanced treatment response and tumor regression. Conversely, PGF exhibited contrasting effects, hinting at a potential adverse influence on treatment outcomes.
Myelin oligodendrocyte glycoprotein antibody-associated disease presenting as abnormal swallowing: a case report
Authors: Yao Dabao , Nie Luwei , Li Yunjie , Wang Jingxuan , Zhang Ping , Li Zhijun , Pan Chao , Liu Na , Tang Zhouping
Published: October 2024

Background
Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a newly classified demyelinating disease caused by the production of autoantibodies against myelin oligodendrocyte glycoprotein (MOG). The initial clinical symptoms of MOGAD are varied, and MOGAD only presenting with abnormal swallowing function is rarely reported. The wide range of clinical phenotypes and diverse imaging features make the diagnosis of MOGAD difficult.
Case presentation
Herein, we report a case of MOGAD presented with 6 years of non-progressive mild difficulty swallowing, with episodes of choking and nausea. The patient was originally suspected of having neoplastic lesions and possibly lymphoma based on symptoms and radiologic manifestations. However, the neuropathological findings of brain biopsy showed demyelination without tumor cells. Further serum and cerebrospinal fluid testing was performed using a cell-based assay, which showed positive results for MOG antibodies. Neurological symptoms improved following steroid treatment. No recurrence of symptoms occurred but the lesions remained during the 2-year follow-up.
Conclusions
This report helps to extend the symptom spectrum of MOGAD and raise awareness of the importance of differentiating between neoplastic and demyelinating lesions. Imaging suggests neoplastic lesions should be differentiated from demyelinating diseases.
Facultative diapause behavior of pink bollworm, PectinophoragossypiellaSaunders (Lepidoptera: Gelechiidae) in South Indian populations
Authors: Mahesh Nandyapur , Bheemanna M. , Prabhuraj Aralimarad , Gaddanakeri Somashekhar
Published: October 2024

Background
The pink bollworm (PBW) of cotton, Pectinophora gossypiella Saunders is one of the serious pests in the Bt cotton era of India. Development of resistance to insecticides and behavioral adaptations are the main factors attributed for its severity. The North Indian populations of PBW are reported to be undergoing facultative diapause, whereas, no clear reports are available on the diapause pattern of South Indian populations. Hence, the diapause behavior of south Indian populations was studied at the UAS, Raichur, Karnataka.
Results
Populations collected from different parts of Karnataka, South India exhibited diapause behavior though the intensity varied. A significant variation in the morphometry of larval, pupal and adult populations of diapause and non-diapausing group was observed. Similar variation was also noticed in the biochemical parameters. Higher levels of amino acids, fats, proteins, and nitrogen were recorded in diapausing population compared to non-diapause population. Enzyme activities such as LDH, AP, GPT, and GOT varied between diapause and non-diapause population. The diapause behavior in South Indian PBW population was initiated from February, peaked in March, and ceased by May. Most larvae terminated diapause in November, coinciding with peak boll formation in cotton.
Conclusion
South Indian population of cotton pink bollworm exhibit facultative diapasue behavior. A clear cut morphological and physiological differentiation was recorded between diapusing and non-diapasuing individuals of the species. An in-depth study on the factors influencing the initiation, regulation and termination of diapasue in PBW need to be carried out.
Applicability of a microRNA-based dynamic risk score for type 1 diabetes
Authors: Hardikar Anandwardhan , Joglekar Mugdha , Wong Wilson , Kunte Pooja , Kulkarni Reshmi , Ahmed Ikhlak , Pham Nhan , Coles Madilyn , Kaur Simranjeet , Pant Aniruddha , Alshabeeb-Akil Ammira , Donaghue Kim , Jenkins Alicia , Craig Maria , Shapiro James , Hague William , Yajnik Chittaranjan , Ma Ronald , Davies Elizabeth , Jones Tim , Gitelman Stephen , Pociot Flemming ,   PREDICT T1D Study Group
Published: October 2024

Identifying biomarkers of functional β-cell loss is critical in risk stratification for Type 1 Diabetes (T1D). We report a microRNA-based dynamic (responsive to environment) risk score developed using multi-center, multi-ethnic/country (“multi-context”) cohorts. Discovery (wet-lab and dry-lab) analysis identified 50 microRNAs that were measured across n=2,204 individuals from four contexts (4C=AUS/Australia, DNK/Denmark, HKG/Hong Kong SAR China, IND/India). A microRNA-based dynamic risk score (DRS) was generated (DRS4C), which effectively stratified individuals with/without T1D. Generative artificial intelligence (GAI) was used to create an enhanced (e)DRS4C that showed AUC >0.84 on an independent Validation set (n=313) from AUS, IND and NZL and predicted future exogenous-insulin requirement in islet transplantation recipients from Canada (CAN). In another T1D therapy, this microRNA signature stratified 1-year response to imatinib based on their profile at the study baseline. Utilizing machine learning and GAI, this study identified and validated a microRNA-based DRS for T1D stratification and treatment efficacy prediction.
Natural Nanoparticle complexes at water-water interfaces
Authors: Rojas Orlando , Wang Han , Lu Yi , Bai Long , Zhu Mengqi , Liu Xiyue , Li Zhiguo , Liu Yang , Li Wei , Liu Shouxin
Published: October 2024

Aqueous two-phase systems (ATPS) with multifunctional attributes have significant promise as biomimetic materials, but current approaches do not harness their full potential. We show that ATPSs can be stabilized to form microcapsules by the interfacial assembly of chitin nanofibers (ChNF) and rod-like cellulose nanocrystals (CNC). The high structural stability of the ChNF/CNC complex integrates permeability and transport across the membrane at the liquid/liquid interface. Driven by density and osmotic stress gradients, the microcapsules show switchable motility, including cyclic meniscus-climbing and subsurface transport. These observations demonstrate a self-regulating system with potential for cargo transfer, cell biomimicry, as well as ATPS-based microreactors and microrobots.
Incidence and Predictors of Mortality Among Persons With Drug Resistant Tuberculosis, and HIV, Mozambique (2015-2020)
Authors: Nacarapa Edy , Jose Benedita , Munyangaju Isabelle , Osório Dulce , Ramos-Rincon Jose-Manuel
Published: October 2024

Drug Resistance Tuberculosis (DRTB) is associated with a high risk of mortality during treatment. This study aims to describe the incidence and baseline characteristics associated with mortality in persons with drug resistance tuberculosis (P-DRTB) in a rural setting, in Mozambique. We analyzed cohort data collected retrospectively from paper medical files and electronic medical records of persons with DRTB (P-DRTB) who were routinely treated at Carmelo Hospital of Chokwe (Gaza province, Mozambique), from 1st January 2015 to 31st December 2020. Kaplan-Meier survival curves and adjusted Cox regression analyses were used to model the time to death and associated factors of mortality. Overall, 151 P-DRTB contributed to a total number of 1812 person-months (PM) of treatment follow-up. The overall mortality rate was 1.9 per 100 person-months (95% confidence interval [CI]: 1.3–2.1). Adjusted Cox regression predicted higher risk of mortality in those treated with DRTB injectable SLD, (adjusted hazard ratio [aHR] 3.72, 95% CI 1.23–11.22, p = 0.020), had a parenchymal lesion with more than 50% fibrosis (aHR 3.06, 95% CI 1.38–6.79, p = 0.006), presented right ventricular dysfunction on the venous cardio ultrasound (aHR 3.18, 95% CI 1.15–8.83, p = 0.026), and manifested baseline hemoglobin (Hgb) = 8.0–9.9 g/dL as well Hgb < 7.9 g/dL (aHR 2.82, 95% CI 1.09–7.27, p = 0.032; aHR 3.06, 95%CI 1.24–7 .51, p = 0.015) respectively. However, lower risk of mortality was predicted in those who had an optimal immunovirological response to ART (aHR 0.18, 95% CI 0.04–0.93, p = 0.040). Kaplan-Meier analysis showed higher cumulative incidence of mortality after 3 months of follow-up, above 26% in those with immunovirological failure to ART therapy p = 0.006), 45% with Hgb < 7.9g/dL (p < 0.001), 23% in treated with injectables-based drugs (p = 0.03), 39% with parenchymal lesion > 50% fibrosis on the chest X-ray (test p = < 0.001), 56% with right ventricular dysfunction (p = 0.003). Mortality risk among P-DRTB was higher in those with anemia, injectable DRTB medications, lung lesions > 50% fibrosis, and right ventricular dysfunction.
Menopausal Hormone Therapy and Risk of Neuropsychiatric Disease: A Drug Target Mendelian Randomisation Study
Authors: Schindler Louise , Gill Dipender , Oppenheimer Hannah , Barth Claudia , Andreassen Ole , Draganski Bogdan , Westlye Lars T. , Topiwala Anya , de Lange Ann-Marie
Published: October 2024

The effects of menopausal hormone therapy (MHT) on neurological and psychiatric disease risk have shown conflicting results across epidemiological and clinical studies. Given the widespread propagation of MHT among females experiencing menopausal symptoms, clarifying causality and impact is critical for informed decision-making. Although randomised controlled trials can establish causal effects, they are associated with significant costs and often of limited duration. Here, we present the first study using Mendelian randomisation (MR) to estimate causal effects of MHT on key neuropsychiatric and brain structural outcomes. To mimic the effects of MHT on oestrogen receptors (ER) α and β, we leveraged genetic variations located in the ESR1 and ESR2 genes that were significantly associated with positive controls as instrumental variables. In two-sample MR analyses, we used GWAS data on late-onset Alzheimer's disease (AD), key brain structural endophenotypes (cortical grey matter volume, hippocampal volume, and white matter hyperintensity volume), as well as depression and anxiety as outcomes, adjusting the p-values for multiple comparisons using false discovery rate correction with an α level of 0.05. There was no evidence for an impact of genetically proxied perturbation of ERα (β = -0.08, 95% CI [-0.34, 0.20], p=0.69 and β = -0.005, 95% CI [-1.45, 1.44], p=1.00) and ERβ (β = 0.35, 95% CI [-0.77, 1.47], p=0.69) on AD risk. Similarly, we found no significant impact of genetically proxied perturbation of ERα and ERβ on associated brain structural outcomes. Genetically proxied perturbation of ERβ was significantly related to higher depression risk (β = -0.66, 95% CI [-0.99, -0.32], p=0.002), but not anxiety risk. Our study provides support for a causal impact of genetically predicted oestrogen receptor perturbation on increased risk of depression, highlighting the importance of considering the implications of targeting these proteins with MHT, for psychiatric outcomes in clinical contexts. However, there was no support for either a harmful or protective causal effect of MHT on AD risk.
Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems
Authors: Ken Pao-Yi , Wu Chao-Chin
Published: October 2024

Deep Learning Automatic Hyperparameter Tuning plays a crucial role in advancing Artificial Intelligence applications, eliminating the need for complex expertise and costly manual operations. Ray Tune, developed by the University of California, Berkeley, has gained widespread adoption among notable companies like Amazon and Uber. In contrast to large enterprises, the hardware commonly used by the general public is often a mix of old and new machines with varying specifications, creating a highly heterogeneous computing environment. This study focuses on optimizing the utilization of heterogeneous resources during machine learning training to improve overall system performance. Through experiments and analysis utilizing Ray Tune, two optimization strategies are explored for different configurations: checkpoint location optimization and a scheduling strategy for consolidating heterogeneous resources. Experimental results show that, with reasonable configurations, storing checkpoints in both main memory and external storage can effectively reduce overall training time. Adopting the heterogeneous resource consolidation scheduling strategy and dynamically allocating tasks based on the computing capabilities of each configured node results in a significant 2.36-fold improvement in overall training time. These optimization strategies offer valuable insights into effectively leveraging heterogeneous resources in automatic hyperparameter tuning.
Interaction between amorphous and crystalline SiO2 with ZnO obtained by various methods: implications for rhodamine 6G elimination
Authors: Daza-Gómez Lucy-Caterine , Escobar Brandon Enrique Vargas , Díaz David , García-Peña Nidia Guadalupe , Redón Rocío
Published: October 2024

This study investigates ZnO nanoparticles synthesized via different methods, consistently revealing a hexagonal wurtzite crystalline phase using XRD, Raman, and XPS analyses. The use of CTAB in the precipitation method resulted in smaller particles. ZnO nanoparticles supported on amorphous SiO2 exhibited reduced size due to the distribution of the precursor salt. rhodamine 6G adsorption of was observed to be higher on amorphous supports, attributed to increased deprotonation of silanol groups. Furthermore, it was observed that the nature of the support, whether amorphous or crystalline, significantly influenced photocatalytic activity. While amorphous supports mainly facilitated rhodamine 6G removal through adsorption, while crystalline supports favored degradation. The concentration increases in supported catalyst affected both degradation and adsorption, leading to reduced photodegradation efficiency without significant changes in adsorbed rhodamine 6G. Although rhodamine 6G degradation was lower compared to unsupported ZnO nanoparticles, only 10% of the supported sample consisted of active ZnO. ZnO nanoparticles on crystalline SiO2 showed minimal reduction in photocatalytic activity after five cycles, indicating their practical advantages for applications.
Dogmatik versus Rechtspolitik? Rechtshistorische Überlegungen zur Regulierung im Wirtschaftsstrafrecht
Authors: Müller Maximilian Erdmann
Published: October 2024

Seit dem letzten Drittel des 20. Jahrhunderts findet sich die Gesetzgebung im Bereich des Wirtschaftsstrafrechts aufgrund der Komplizierung der Regelungsmaterie zunehmend in Sackgassen wieder. Vor diesem Hintergrund gewinnt die Justiz als Regulierungsakteur erheblich an Bedeutung. Gerade der weit und unbestimmt gefasste Untreuetatbestand – die Allzweckwaffe des Wirtschaftsstrafrechts – bietet dabei gerichtlicher Regulierung ein Einfallstor. Die justizielle Aufarbeitung der Siemens- Korruptionsaffäre kann dies eindrücklich illustrieren. Dabei befindet sich die Justiz, so wie auch die Legislative, in einem Spannungsfeld zwischen Dogmatik und Rechtspolitik.
Quantifying Temperature Effects on Development Rate and Plant Quality of Compact Container-grown Tomato
Authors: Humphrey Morgan , Dickson Ryan W. , Padhye Sonali
Published: October 2024

Mean daily temperature effects on plant development rates and quality were evaluated for compact container-grown tomato (Solanum lycopersicum). Compact tomato varieties ‘Siam’ and ‘Red Velvet’ were grown in greenhouses at 18 to 26 °C (Expt. 1) and 20 to 30 °C (Expt. 2) under supplemental high-pressure sodium lighting and 16-h photoperiod. The number of days to first open flower, first ripe fruit and from flower to ripe fruit were measured and development rates calculated by taking the reciprocal (e.g., 1/d). Temperature effects were predicted by fitting a linear (for first open flower) and a nonlinear exponential function (for first ripe fruit and between first open flower and ripe fruit), which included base temperature (Tmin) and maximum developmental rate (Rmax, 1/d) parameters. Plant quality attributes were measured in Expt. 2. As temperature increased, the time to flower and fruit decreased (i.e., developmental rates increased) for both varieties. Estimated Tmin was 8.7 °C for ‘Siam’ and 11.4 °C for ‘Red Velvet’, whereas Rmax was similar between cultivars (0.030 at fruit and 0.037 from flower to fruit). ‘Siam’ and ‘Red Velvet’ grown at ≈25 °C had a relatively short crop time, compact canopy, adequate fruit size, and a high number of fruits per plant at finish. Compact tomatoes are new crops being grown by greenhouse floriculture operations for ornamental and edible value, and the information from this study can help growers schedule these crops to meet critical market windows and determine the impacts of changing growing temperature on crop timing and quality.
Performance of Improved Bell Pepper Cultivars in Southern Georgia for Mature Green Fresh Market Production
Authors: Kumari Manisha , Dutta Bhabesh , Coolong Timothy , Torrance Ty , Shealey Justin , Dawson Joshua , McAvoy Theodore
Published: October 2024

Three research trials were performed over two spring seasons to assess the performance of bell pepper (Capsicum annuum) cultivars in southern Georgia, USA. Studies were conducted in commercial production fields in Echols and Lowndes counties, which are major bell pepper production areas in Georgia, USA. Eleven cultivars were evaluated, and 10 newer bell pepper cultivars with improved disease resistances for bacterial leaf spot races 1 to 10, tomato spotted wilt virus, and/or phytophthora were compared with ‘Aristotle’, which historically was the most widely grown bell pepper hybrid in the southeastern United States. Our goal was to determine how the new and improved cultivars compared with ‘Aristotle’ for total yield, fruit size distribution, and unmarketable defects in the absence of disease pressure. There were significant differences in fruit size, marketable fruit, and culls between cultivars contrasting their adaptability for production in southern Georgia. The top performers for yield and preferred fruit size for fresh market bell pepper spring production in southern Georgia were ‘Autry’, ‘Standout’, and ‘Antebellum’. Yields and fruit size distribution of these cultivars were similar to ‘Aristotle’ but may be preferred in certain situations due to additional disease resistance. The cultivar, SV 3255 also had higher yields and low defects but had smaller average fruit sizes.
Short-term prediction of trimaran load based on data driven technology
Authors:   Navigation College, Dalian Maritime University, Dalian 116026, China, Tang Haoyun , Zhu Rui ,   Navigation College, Dalian Maritime University, Dalian 116026, China, Wan Qian ,   College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China,   Dalian shipbuilding industry Co., Ltd, Dalian 116026, China, Ren Deyuan ,   Navigation College, Dalian Maritime University, Dalian 116026, China
Published: October 2024

Due to the complex flow interference by side hulls, load prediction has been one of the obstacles to achieve structural health monitoring and intelligent navigation of trimarans. According to the machine learning theory, a short-term prediction method towards trimaran loads is studied by using an optimized data-driven model. In the research, the monitoring data from a trimaran model test is applied for the training and testing of long short-term memory (LSTM) neural network. The impact analysis on the factors such as input length, neuron number, artificial neural network (ANN) optimizer, and output scope, are taken. To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). Through the comprehensive comparison of statistical indicators at different positions and wave environment, the VMD-LSTM model is selected by considering its highest load prediction ability among these artificial neural network models. The research will improve the load prediction accuracy in the structural health monitoring systems and offer effective technical support for intelligent unmanned trimarans.
Können Roboter Menschen moralisch verletzen?
Authors: Manzeschke Arne
Published: October 2024

U mjeri u kojoj su roboti oblikovani kao partneri u društvenoj interakciji ljudi, postavljaju se ozbiljna moralna pitanja. Članak istražuje pitanje mogu li roboti moralno povrijediti ljude. Razradu pitanja počinjem s (1) kratkom refleksijom o tome što se podrazumijeva pod »roboti«. (2) Skiciram što znači biti moralno povrijeđen. U daljem koraku (3) ispituju se razlike među fizičkom, psihičkom i moralnom nanošenju štete. To nas dovodi (4) do pitanja koja vrsta aktera bi mogla biti uzročnik takvog nanošenja štete. Stoga, upućujem (5) na jasno ustanovljenu taksonomiju bogova, ljudi, životinja, biljaka i strojeva. Doći ću do zaključka (6) da status sposobnosti za djelovanje nekoga robota zavisi od odluke ljudi o tome kako ga žele konstituirati. Stoga bismo kao ljudska društva trebali biti dobro informirani kada razmišljamo o ovom važnom pitanju. Zaista je važno na koji način donosimo odluke o ovom pitanju koje utječe na naše samorazumijevanje i na strukturu društvenoga života.
“tapete verde” al oriente de la Zona Metropolitana de la Ciudad de México
Authors: Montiel Rogel Alma Patricia
Published: October 2024

El objetivo de las siguientes páginas es analizar, a través del uso de datos derivados de trabajo de campo, de reportes estadísticos gubernamentales y de una revisión bibliográfica, el fenómeno urbano-rural del municipio de Texcoco. Esta perspectiva parte de la observación de sus dinámicas territoriales considerando su contexto dentro del área oriente de la Zona Metropolitana de la Ciudad de México. En otros estudios, esto aparece desdibujado, pero aquí es fundamental para la comprensión de la peculiar historia urbano-rural de Texcoco. Se parte de la inquietud por comprender cuáles fueron las causas que conformaron las actuales dinámicas urbano-rurales del municipio, que contrastan con las de otras demarcaciones. En los municipios aledaños hay una densidad industrial, poblacional y de vivienda que ha impactado en el uso de suelo vegetal, forestal y agrícola a tal grado que la expansión urbana ha alcanzado los cerros. En Texcoco se constata la conservación de este tipo de uso del suelo y la pervivencia de fenómenos rurales. Como hipótesis, se considera que la presencia de una industria ligera de tipo textil, la falta de recepción de migración interestatal campo-ciudad y el modelo de vivienda unifamiliar y en condominio fueron las causantes de las actuales dinámicas vistas en sus diferentes franjas urbano-rurales.
Structural Design of Building’s Superstructure
Authors: Cheng Y.M. , Law C.W.
Published: October 2024

This chapter introduces the various structural elements and systems that are
commonly adopted for the structural design of buildings, especially tall buildings. The
advantages and limitations of each system are discussed with respect to the structural behaviour
and performance. Sufficient in-depth discussions are provided so that the readers can make
choice(s) on the structural system(s) that is suitable for a particular project.
Loads on Superstructure and their Statistical Nature
Authors: Cheng Y.M. , Law C.W.
Published: October 2024

This chapter discusses the various types of loadings that are required to be considered
in the design of buildings. The loadings include dead loads and various types of live loads, wind
loads, water and soil loads, seismic loads, snow loads, temperature loads, and others. There are
various considerations behind these loadings, which are discussed in depth in this chapter.
Various load combinations will also be discussed with respect to British codes, Euro codes, and
ASCE codes.
Ductility, Robustness, Durability and Fire Resistance Design Considerations in Buildings
Authors: Cheng Y.M. , Law C.W.
Published: October 2024

This chapter discusses the ductility, durability, robustness, and fire resistance
considerations for building designs which are usually not well-treated in many texts. The
considerations will include both reinforced concrete and steel buildings.
Excavation and Lateral Support System (ELS)
Authors: Cheng Y.M. , Law C.W.
Published: October 2024

In this chapter, different types of retaining wall systems are introduced. The method
of installation of these wall types is discussed in detail, with the support of many field photos
and construction method statements from contractors. The methods of excavation are illustrated
with some practical examples from Hong Kong. Following the construction aspects, the theory
behind the lateral earth pressure, water pressure and the methods of analysis for the staged
excavation are discussed, while the problems that may arise during deep excavation are
illustrated with practical examples. A sample monitoring scheme for a project is also included
in this chapter to help the readers appreciate the use of IOT monitoring during construction,
which is now becoming popular for many large-scale construction works.
Risk Factors Analysis for Rapid In-Hospital Mortality among Covid-19 Patients in a Tertiary Care Hospital in Indonesia
Authors: Febriawati Juwita , Putra Ngakan Putu Parsama , Listyoko Aditya Sri , Djajalaksana Susanthy
Published: October 2024

Systemic inflammation plays an important role in pathogenesis of Covid-19, especially in development of ARDS which is characterized by decrease of PaO2/FiO2 ratio. CRP and procalcitonin are inflammatory markers that are closely associated with severity and mortality of Covid-19. Although several studies have addressed benefit of CRP and procalcitonin as markers on Covid-19 severity, the benefit of these inflammatory markers for in-hospital mortality remain inadequately understood. The aim of this study was to analyze PaO2/FiO2 ratio,comorbidity,CRP,and procalcitonin as risk factors that affect time of in-hospital mortality Covid-19 patient. This was a retrospective observational cohort study of 250 Covid-19 patients who died during hospitalization and data was retrieved from medical record. Laboratory data was collected from three different times, including at time of admission,third day of hospital care,and before patient’s death. Data were analyzed using Chi square test,Mann Whitney test,Wilcoxon test, Friedman test, and binary logistic regresion. There were significant differences between CRP and procalcitonin at admission and time of in-hospital mortality (p<0.001; p=0.007). Binary logistic regression  analysis revealed significant relationship between CRP and time of in-hospital mortality with p=0.007. ROC curve showed optimal threshold of 11.75mg/L with sensitivity 72.3%; specificity 59.6%, RR 3.24(95% CI: 1.84-5.70). Significant changes were observed regarding PaO2/FiO2 ratio,CRP,and procalcitonin at admission as compared to before patient’s death with p<0.001;p=0.017;p<0.001 respectively. This study showed significant decrease of PaO2/FiO2 ratio, elevated CRP and procalcitonin at admission as compared to before patient’s death. The increase of CRP could serve as predictor for time of in-hospital mortality for Covid-19 patient.
OPTIMIZING VEHICULAR NETWORK MANAGEMENT USING CONVOLUTIONAL NEURAL NETWORKS
Authors:   K. C. College of Engineering, India, Maurya Mahesh , T Varun ,   Anna University, India, latha K. Sree ,   St Peter's Engineering College, India, Kumar Atul ,   Dr. D. Y. Patil B-School, Pune, India
Published: October 2024

CNN have been utilized in many domains and have revolutionized the field of computer vision, natural language processing and vehicular network management. CNNs are loaded with a number of advantages over the current methods of controlling vehicular networks. For instance, they can effectively handle the dynamic behavior of vehicular network due to their ability to learn recognition patterns. Additionally, CNNs are equipped with the capability to perform feature extraction along with its learning and integrating abilities, which can be highly advantageous for vehicular network management. Furthermore, they enable for parametric optimization thus increasing the speed of convergence with low-cost computational resources. Thus, CNNs are a promising approach for highly reliable communication and control of vehicular networks.
USER-CENTRIC ADAPTIVE MULTIMEDIA STREAMING IN INTERACTIVE COMMUNICATION NETWORKS USING SHANNON-FANO GENETIC ALGORITHM
Authors:   St. Joseph’s College of Engineering, India, Dhavamani Logeshwari , A Kaliappan ,   P.A. College of Engineering and Technology, India, M Sakthivel ,   Malla Reddy College of Engineering, India, V Balaji ,   Mai Nefhi College of Engineering and Technology, Eriteria
Published: October 2024

In today’s rapidly evolving digital landscape, interactive communication networks play a pivotal role in facilitating real-time interactions among users. One of the critical challenges in these networks is ensuring the seamless delivery of multimedia content that caters to the diverse needs and preferences of individual users. This research endeavors to address this challenge by introducing a novel approach, where it places user satisfaction at its core, leveraging adaptive streaming techniques to dynamically adjust multimedia content delivery. By considering parameters such as network conditions, device capabilities, and user preferences, it optimizes the streaming experience in real-time. A key innovation lies in the integration of Shannon-Fano coding principles and genetic algorithms. Shannon-Fano coding enhances data compression efficiency, reducing bandwidth consumption, while genetic algorithms fine-tune the adaptive streaming parameters for each user. Our experimentation and evaluations demonstrate the effectiveness of this approach, showcasing improved multimedia streaming quality, reduced latency, and efficient bandwidth utilization. The synergy of user-centricity, adaptive streaming, Shannon-Fano coding, and genetic algorithms presents a promising avenue for enhancing multimedia communication in interactive networks.
ADVANCEMENT IN LOCALIZATION TECHNIQUES USING PRECODERS FOR ULTRA WIDE-BAND SYSTEMS
Authors:   SRM Valliammai Engineering College, India, C Saravanakumar , Rao Allanki Sanyasi ,   Christu Jyothi Institute of Technology and Science, India, C Harini ,   Malla Reddy Engineering College, India, Velusamy Saravanan ,   University of Technology and Applied Sciences, Sultanate Oman
Published: October 2024

In the era of rapidly expanding wireless communication systems, the demand for high-performance, low-latency, and energy-efficient solutions is paramount. One technology that has emerged as a transformative force in addressing these requirements is Massive Multiple-Input Multiple-Output (Massive MIMO) precoding. This abstract delves into the key aspects of Massive MIMO precoding, highlighting its role in enhancing spectral efficiency, mitigating interference, and improving the overall performance of wireless networks. Massive MIMO precoding leverages a substantial number of antennas at the transmitter, allowing for the creation of highly focused spatial beams. These beams can be dynamically optimized to cater to the specific requirements of individual users or devices, maximizing the spectral efficiency by spatially multiplexing multiple streams. This technique offers significant advantages in terms of increasing network capacity and achieving higher data rates, especially in dense network scenarios. Furthermore, Massive MIMO precoding excels in interference mitigation. By spatially directing signals toward intended recipients and steering nulls towards interferers, it reduces the impact of co-channel interference, enhancing network reliability and quality of service. This is particularly valuable in scenarios where network congestion and interference pose significant challenges, such as urban environments and crowded event venues. The research delves into the role of Massive MIMO precoding in improving the signal-to-noise ratio, which directly translates to extended coverage areas and reduced power consumption. Additionally, we explore the implications of Massive MIMO precoding in enabling efficient communication in massive Internet of Things (IoT) deployments and its potential for 5G and beyond. Massive MIMO precoding is poised to reshape the wireless communication landscape. It promises to deliver unprecedented gains in spectral efficiency, interference management, and energy efficiency. As the demand for high-speed, reliable, and ubiquitous connectivity continues to surge, this research plays the pivotal role that Massive MIMO precoding plays in meeting these demands, ushering in a new era of wireless communication.
WIRELESS TRAFFIC AND ROUTING ENHANCEMENT USING EMPEROR PENGUIN OPTIMIZER GUIDED BY CONDITIONAL GENERATIVE ADVERSARIAL NETS
Authors:   Prathyusha Engineering College, India, Chandran K. Prabhu , P.T. Kalaivaani ,   Vivekanandha College of Engineering for Women, India, Kavididevi Venkatesh ,   Vardhaman College of Engineering, India, M Ganesha ,   A J Institute of Engineering and Technology, India
Published: October 2024

The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.
SECURING IT NETWORKING ENVIRONMENT IN CRAN USING DEHAENE–CHANGEUX MODEL DRIVEN MOTH-FLAME OPTIMIZATION
Authors:   Amrutvahini College of Engineering, Sangamner, India, Paikrao Rahul Laxmanrao , Paikrao Prashant Laxmanrao ,   Government College of Engineering, Amravati, India
Published: October 2024

In the dynamic landscape of telecommunications, the evolution of Communication Radio Access Networks (CRAN) has introduced unprecedented challenges to the security of IT networking environments. As the demand for high-speed connectivity and seamless data transmission grows, safeguarding CRAN becomes paramount. With the proliferation of cyber-attacks and the complexity of CRAN architecture, conventional security measures prove insufficient, necessitating an innovative and adaptive approach. Existing methodologies lack the adaptability required to combat emerging threats effectively. This research bridges this gap by proposing the integration of the Dehaene–Changeux Model, renowned for its applicability in cognitive neuroscience, with Moth-Flame Optimization, a nature-inspired algorithm known for its efficiency in solving complex optimization problems. This research addresses the pressing need for a robust security framework using the Dehaene–Changeux Model Driven Moth-Flame Optimization approach. It elucidates the utilization of the Dehaene–Changeux Model to mimic cognitive responses, coupled with Moth-Flame Optimization for real-time adaptability. These models form a dynamic defense mechanism against evolving security threats in the CRAN environment. Results obtained from simulation and testing validate the efficacy of the proposed security model. The adaptive nature of the Dehaene–Changeux Model, combined with the optimization capabilities of Moth-Flame Optimization, showcases a significant enhancement in CRAN security. The research contributes a pioneering solution to fortify IT networking environments in CRAN, ensuring resilience against current and future cyber threats.
DIFFERENTIAL EVOLUTION FRAMEWORK TO IMPROVE THE NETWORK LIFETIME OF IOT-MANETS
Authors:   Kalaignar Karunanidhi Institute of Technology, India, Chandrasekaran Suresh
Published: October 2024

In the dynamic landscape of Internet of Things Mobile Ad Hoc Networks (IOT-MANETs), optimizing the network lifetime is paramount for sustained and efficient operation. The research begins by recognizing the inherent complexities of IOT-MANETs and the inadequacies of current methodologies. The identified research gap revolves around the lack of a comprehensive framework specifically tailored to optimize network lifetime in these dynamic environments. To bridge this gap, the proposed methodology leverages the powerful optimization capabilities of Differential Evolution—a nature-inspired algorithm that mimics the process of natural selection. This research endeavors to address the pressing challenge of enhancing the longevity of IOT-MANETs by proposing a novel framework based on Differential Evolution (DE). The DE-based framework employs a systematic approach to adaptively optimize network parameters, considering factors such as energy consumption, routing efficiency, and communication reliability. The methodology integrates seamlessly with the inherent characteristics of IOT-MANETs, ensuring adaptability to changing network dynamics. Rigorous simulations and experiments validate the effectiveness of the proposed framework, demonstrating substantial improvements in network lifetime compared to existing methods. The results underscore the significance of the DE-based framework in substantially extending the operational lifespan of IOT-MANETs. This research contributes a valuable tool to the arsenal of solutions for enhancing the sustainability and efficiency of IoT-based mobile ad hoc networks, paving the way for more resilient and long-lasting deployments.
CAUSAL CONVOLUTION EMPLOYING ALMEIDA–PINEDA RECURRENT BACKPROPAGATION FOR MOBILE NETWORK DESIGN
Authors:   Sona College of Technology, India, Dakshinamurthi Vidyabharathi , Ali Syed Ibad ,   Ajeenkya DY Patil University, India, T Karthikeyan ,   University of Technology and Applied Sciences - Salalah, Sultanate of Oman, Kulkarni Nanda Satish ,   Siddhant College of Engineering, India
Published: October 2024

Designing efficient mobile networks is crucial for meeting the growing demand for high-speed, reliable communication. However, existing convolutional neural network (CNN) architectures face challenges in capturing temporal dependencies, hindering their performance in mobile network design. The introduction highlights the increasing importance of mobile networks and identifies the limitations of current CNN architectures in capturing temporal dynamics. The problem statement emphasizes the need for an enhanced model that can effectively address temporal dependencies in mobile network design. This research addresses this problem by proposing a novel approach: Causal Convolution employing Almeida–Pineda Recurrent Backpropagation (CC-APRB). The causal convolution captures temporal dependencies by considering only past and present inputs, while the recurrent backpropagation optimizes the model parameters based on sequential data. The integration of these techniques aims to enhance the model ability to capture temporal features in mobile network data. The results indicate significant improvements in the performance of the CC-APRB model compared to traditional CNN architectures. The model demonstrates enhanced accuracy and efficiency in capturing temporal dependencies, making it well-suited for mobile network design applications.