Search results for: absolute generality

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Petros M. Chronopoulos, Evangelos Z. Astreinidis

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A major issue of the structural assessment and rehabilitation of existing RC structures is the inadequate lap-splicing of the longitudinal reinforcement. Although prohibited by modern Design Codes, the practice of arranging lap-splices inside the critical regions of RC elements was commonly applied in the past. Today this practice is still the rule, at least for conventional new buildings. Therefore, a lot of relevant research is ongoing in many earthquake prone countries. The rehabilitation of deficient lap-splices of RC elements by means of external confinement is widely accepted as the most efficient technique. If correctly applied, this versatile technique offers a limited increase of flexural capacity and a considerable increase of local ductility and of axial and shear capacities. Moreover, this intervention does not affect the stiffness of the elements and does not affect the dynamic characteristics of the structure. This technique has been extensively discussed and researched contributing to vast accumulation of technical and scientific knowledge that has been reported in relevant books, reports and papers, and included in recent Design Codes and Guides. These references are mostly dealing with modeling and redesign, covering both the enhanced (axial and) shear capacity (due to the additional external closed hoops or jackets) and the increased ductility (due to the confining action, preventing the unzipping of lap-splices and the buckling of continuous reinforcement). An analytical and experimental program devoted to RC members with lap-splices is completed in the Lab. of RC/NTU of Athens/GR. This program aims at the proposal of a rational and safe theoretical model and the calibration of the relevant Design Codes’ provisions. Tests, on forty two (42) full scale specimens, covering mostly beams and columns (not walls), strengthened or not, with adequate or inadequate lap-splices, have been already performed and evaluated. In this paper, the results of twelve (12) specimens under fully reversed cyclic actions are presented and discussed. In eight (8) specimens the lap-splices were inadequate (splicing length of 20 or 30 bar diameters) and they were retrofitted before testing by means of additional external confinement. The two (2) most commonly applied confining materials were used in this study, namely steel and FRPs. More specifically, jackets made of CFRP wraps or light cages made of mild steel were applied. The main parameters of these tests were (i) the degree of confinement (internal and external), and (ii) the length of lap-splices, equal to 20, 30 or 45 bar diameters. These tests were thoroughly instrumented and monitored, by means of conventional (LVDTs, strain gages, etc.) and innovative (optic fibre-Bragg-grating) sensors. This allowed for a thorough investigation of the most influencing design parameter, namely the hoop-stress developed in the confining material. Based on these test results and on comparisons with the provisions of modern Design Codes, it could be argued that shorter (than the normative) lap-splices, commonly found in old structures, could still be effective and safe (at least for lengths more than an absolute minimum), depending on the required ductility, if a properly arranged and adequately detailed external confinement is applied.

Keywords: concrete, fibre-Bragg-grating sensors, lap-splices, retrofitting / rehabilitation

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Authors: Denise De Fátima Fernandes Barbosa, Tyane Mayara Ferreira Oliveira, Diego Jorge Maia Lima, Paula Renata Amorim Lessa, Ana Karina Bezerra Pinheiro, Cintia Gondim Pereira Calou, Glauberto Da Silva Quirino, Hellen Lívia Oliveira Catunda, Tatiana Gomes Guedes, Nicolau Da Costa

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The aim of this study is to evaluate the use of Digital Cervicography (DC) in the diagnosis of precancerous lesions related to Human Papillomavirus (HPV). Cross-sectional study with a quantitative approach, of evaluative type, held in a health unit linked to the Pro Dean of Extension of the Federal University of Ceará, in the period of July to August 2015 with a sample of 33 women. Data collecting was conducted through interviews with enforcement tool. Franco (2005) standardized the technique used for DC. Polymerase Chain Reaction (PCR) was performed to identify high-risk HPV genotypes. DC were evaluated and classified by 3 judges. The results of DC and PCR were classified as positive, negative or inconclusive. The data of the collecting instruments were compiled and analyzed by the software Statistical Package for Social Sciences (SPSS) with descriptive statistics and cross-references. Sociodemographic, sexual and reproductive variables were analyzed through absolute frequencies (N) and their respective percentage (%). Kappa coefficient (κ) was applied to determine the existence of agreement between the DC of reports among evaluators with PCR and also among the judges about the DC results. The Pearson's chi-square test was used for analysis of sociodemographic, sexual and reproductive variables with the PCR reports. It was considered statistically significant (p<0.05). Ethical aspects of research involving human beings were respected, according to 466/2012 Resolution. Regarding the socio-demographic profile, the most prevalent ages and equally were those belonging to the groups 21-30 and 41-50 years old (24.2%). The brown color was reported in excess (84.8%) and 96.9% out of them had completed primary and secondary school or studying. 51.5% were married, 72.7% Catholic, 54.5% employed and 48.5% with income between one and two minimum wages. As for the sexual and reproductive characteristics, prevailed heterosexual (93.9%) who did not use condoms during sexual intercourse (72.7%). 51.5% had a previous history of Sexually Transmitted Infection (STI), and HPV the most prevalent STI (76.5%). 57.6% did not use contraception, 78.8% underwent examination Cancer Prevention Uterus (PCCU) with shorter time interval or equal to one year, 72.7% had no cases of Cervical Cancer in the family, 63.6% were multiparous and 97% were not vaccinated against HPV. DC identified good level of agreement between raters (κ=0.542), had a specificity of 77.8% and sensitivity of 25% when compared their results with PCR. Only the variable race showed a statistically significant association with CRP (p=0.042). DC had 100% acceptance amongst women in the sample, revealing the possibility of other experiments in using this method so that it proves as a viable technique. The DC positivity criteria were developed by nurses and these professionals also perform PCCU in Brazil, which means that DC can be an important complementary diagnostic method for the appreciation of these professional’s quality of examinations.

Keywords: gynecological examination, human papillomavirus, nursing, papillomavirus infections, uterine lasmsneop

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Authors: Nicola Colaninno, Eugenio Morello

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The urban environment affects local-to-global climate and, in turn, suffers global warming phenomena, with worrying impacts on human well-being, health, social and economic activities. Physic-morphological features of the built-up space affect urban air temperature, locally, causing the urban environment to be warmer compared to surrounding rural. This occurrence, typically known as the Urban Heat Island (UHI), is normally assessed by means of air temperature from fixed weather stations and/or traverse observations or based on remotely sensed Land Surface Temperatures (LST). The information provided by ground weather stations is key for assessing local air temperature. However, the spatial coverage is normally limited due to low density and uneven distribution of the stations. Although different interpolation techniques such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), or Multiple Linear Regression (MLR) are used to estimate air temperature from observed points, such an approach may not effectively reflect the real climatic conditions of an interpolated point. Quantifying local UHI for extensive areas based on weather stations’ observations only is not practicable. Alternatively, the use of thermal remote sensing has been widely investigated based on LST. Data from Landsat, ASTER, or MODIS have been extensively used. Indeed, LST has an indirect but significant influence on air temperatures. However, high-resolution near-surface air temperature (NSAT) is currently difficult to retrieve. Here we have experimented Geographically Weighted Regression (GWR) as an effective approach to enable NSAT estimation by accounting for spatial non-stationarity of the phenomenon. The model combines on-site measurements of air temperature, from fixed weather stations and satellite-derived LST. The approach is structured upon two main steps. First, a GWR model has been set to estimate NSAT at low resolution, by combining air temperature from discrete observations retrieved by weather stations (dependent variable) and the LST from satellite observations (predictor). At this step, MODIS data, from Terra satellite, at 1 kilometer of spatial resolution have been employed. Two time periods are considered according to satellite revisit period, i.e. 10:30 am and 9:30 pm. Afterward, the results have been downscaled at 30 meters of spatial resolution by setting a GWR model between the previously retrieved near-surface air temperature (dependent variable), the multispectral information as provided by the Landsat mission, in particular the albedo, and Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM), both at 30 meters. Albedo and DEM are now the predictors. The area under investigation is the Metropolitan City of Milan, which covers an area of approximately 1,575 km2 and encompasses a population of over 3 million inhabitants. Both models, low- (1 km) and high-resolution (30 meters), have been validated according to a cross-validation that relies on indicators such as R2, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). All the employed indicators give evidence of highly efficient models. In addition, an alternative network of weather stations, available for the City of Milano only, has been employed for testing the accuracy of the predicted temperatures, giving and RMSE of 0.6 and 0.7 for daytime and night-time, respectively.

Keywords: urban climate, urban heat island, geographically weighted regression, remote sensing

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Authors: Adrian W. Egger, Savvas P. Triantafyllou, Eleni N. Chatzi

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The scaled boundary finite element method (SBFEM) is a semi-analytical numerical method, which introduces a scaling center in each element’s domain, thus transitioning from a Cartesian reference frame to one resembling polar coordinates. Consequently, an analytical solution is achieved in radial direction, implying that only the boundary need be discretized. The only limitation imposed on the resulting polygonal elements is that they remain star-convex. Further arbitrary p- or h-refinement may be applied locally in a mesh. The polygonal nature of SBFEM elements has been exploited in quadtree meshes to alleviate all issues conventionally associated with hanging nodes. Furthermore, since in 2D this results in only 16 possible cell configurations, these are precomputed in order to accelerate the forward analysis significantly. Any cells, which are clipped to accommodate the domain geometry, must be computed conventionally. However, since SBFEM permits polygonal elements, significantly coarser meshes at comparable accuracy levels are obtained when compared with conventional quadtree analysis, further increasing the computational efficiency of this scheme. The generalized stress intensity factors (gSIFs) are computed by exploiting the semi-analytical solution in radial direction. This is initiated by placing the scaling center of the element containing the crack at the crack tip. Taking an analytical limit of this element’s stress field as it approaches the crack tip, delivers an expression for the singular stress field. By applying the problem specific boundary conditions, the geometry correction factor is obtained, and the gSIFs are then evaluated based on their formal definition. Since the SBFEM solution is constructed as a power series, not unlike mode superposition in FEM, the two modes contributing to the singular response of the element can be easily identified in post-processing. Compared to the extended finite element method (XFEM) this approach is highly convenient, since neither enrichment terms nor a priori knowledge of the singularity is required. Computation of the gSIFs by SBFEM permits exceptional accuracy, however, when combined with hybrid quadtrees employing linear elements, this does not always hold. Nevertheless, it has been shown that crack propagation schemes are highly effective even given very coarse discretization since they only rely on the ratio of mode one to mode two gSIFs. The absolute values of the gSIFs may still be subject to large errors. Hence, we propose a post-processing scheme, which minimizes the error resulting from the approximation space of the cracked element, thus limiting the error in the gSIFs to the discretization error of the quadtree mesh. This is achieved by h- and/or p-refinement of the cracked element, which elevates the amount of modes present in the solution. The resulting numerical description of the element is highly accurate, with the main error source now stemming from its boundary displacement solution. Numerical examples show that this post-processing procedure can significantly improve the accuracy of the computed gSIFs with negligible computational cost even on coarse meshes resulting from hybrid quadtrees.

Keywords: linear elastic fracture mechanics, generalized stress intensity factors, scaled finite element method, hybrid quadtrees

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Authors: Saraswati Kerketta

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Tea industry is one of the oldest organised sector of India. It employs roughly 1.5 million people directly. Since the last decade Indian tea industry, especially in the northern region is experiencing worst crisis in the post-independence period. Due to many reason the prices of tea show steady decline. The workers are paid one of the lowest wage in tea industry in the world (1.5$ a day) below the UN's $2 a day for extreme poverty. The workers rely on addition benefits from plantation which includes food, housing and medical facilities. These have been effective means of enslavement of generations of labourers by the owners. There is hardly any change in the tea estates where the owners determine the fate of workers. When the tea garden is abandoned or is closed all the facilities disappear immediately. The workers are the descendants of tribes from central India also known as 'tea tribes'. Alienated from their native place, the geographical and social isolation compounded their vulnerability of these people. The economy of the region being totally dependent on tea has resulted in absolute unemployment for the workers of these tea gardens. With no other livelihood and no land to grow food, thousands of workers faced hunger and starvation. The Plantation Labour Act which ensures the decent working and living condition is violated continuously. The labours are forced to migrate and are also exposed to the risk of human trafficking. Those who are left behind suffers from starvation, malnutrition and disease. The condition in the sick tea plantation is no better. Wage are not paid regularly, subsidised food, fuel are also not supplied properly. Health care facilities are in very bad shape. Objectives: • To study the socio-cultural and demographic characteristics of the tea garden labourers in the study area. • To examine the social situation of workers in sick estates in dooars region. • To assess the magnitude of deprivation the impact of economic crisis on abandoned and closed tea estates in the region. Data Base: The study is based on data collected from field survey. Methods: Quantative: Cross-Tabulation, Regression analysis. Qualitative: Household Survey, Focussed Group Discussion, In-depth interview of key informants. Findings: Purchasing power parity has declined since in last three decades. There has been many fold increase in migration. Males migrates long distance towards central and west and south India. Females and children migrates both long and short distance. No one has reported to migrate back to the place of origin of their ancestors. Migrant males work mostly as construction labourers and as factory workers whereas females and children work as domestic help and construction labourers. In about 37 cases either they haven't contacted their families in last six months or are not traceable. The families with single earning members are more likely to migrate. Burden of disease and the duration of sickness, abandonment and closure of plantation are closely related. Death tolls are likely to rise 1.5 times in sick tea gardens and three times in closed tea estates. Sixty percent of the people are malnourished in the sick tea gardens and more than eighty five per cent in abandoned and sick tea gardens.

Keywords: migration, trafficking, starvation death, tea garden workers

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Authors: Sofia Quaglia, Carmine Di Martino, Brendan Tierney

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Artificial Intelligence (A.I.) constitutes various aspects of modern life, from the Machine Learning algorithms predicting the stocks on Wall streets to the killing of belligerents and innocents alike on the battlefield. Moreover, the end goal is to create autonomous A.I.; this means that the presence of humans in the decision-making process will be absent. The question comes naturally: when an A.I. does something wrong when its behavior is harmful to the community and its actions go against the law, which is to be held responsible? This research’s subject matter in A.I. and Robot Ethics focuses mainly on Robot Rights and its ultimate objective is to answer the questions: (i) What is the function of rights? (ii) Who is a right holder, what is personhood and the requirements needed to be a moral agent (therefore, accountable for responsibility)? (iii) Can an A.I. be a moral agent? (ontological requirements) and finally (iv) if it ought to be one (ethical implications). With the direction to answer this question, this research project was done via a collaboration between the School of Computer Science in the Technical University of Dublin that oversaw the technical aspects of this work, as well as the Department of Philosophy in the University of Milan, who supervised the philosophical framework and argumentation of the project. Firstly, it was found that all rights are positive and based on consensus; they change with time based on circumstances. Their function is to protect the social fabric and avoid dangerous situations. The same goes for the requirements considered necessary to be a moral agent: those are not absolute; in fact, they are constantly redesigned. Hence, the next logical step was to identify what requirements are regarded as fundamental in real-world judicial systems, comparing them to that of ones used in philosophy. Autonomy, free will, intentionality, consciousness and responsibility were identified as the requirements to be considered a moral agent. The work went on to build a symmetrical system between personhood and A.I. to enable the emergence of the ontological differences between the two. Each requirement is introduced, explained in the most relevant theories of contemporary philosophy, and observed in its manifestation in A.I. Finally, after completing the philosophical and technical analysis, conclusions were drawn. As underlined in the research questions, there are two issues regarding the assignment of moral agency to artificial agent: the first being that all the ontological requirements must be present and secondly being present or not, whether an A.I. ought to be considered as an artificial moral agent. From an ontological point of view, it is very hard to prove that an A.I. could be autonomous, free, intentional, conscious, and responsible. The philosophical accounts are often very theoretical and inconclusive, making it difficult to fully detect these requirements on an experimental level of demonstration. However, from an ethical point of view it makes sense to consider some A.I. as artificial moral agents, hence responsible for their own actions. When considering artificial agents as responsible, there can be applied already existing norms in our judicial system such as removing them from society, and re-educating them, in order to re-introduced them to society. This is in line with how the highest profile correctional facilities ought to work. Noticeably, this is a provisional conclusion and research must continue further. Nevertheless, the strength of the presented argument lies in its immediate applicability to real world scenarios. To refer to the aforementioned incidents, involving the murderer of innocents, when this thesis is applied it is possible to hold an A.I. accountable and responsible for its actions. This infers removing it from society by virtue of its un-usability, re-programming it and, only when properly functioning, re-introducing it successfully

Keywords: artificial agency, correctional system, ethics, natural agency, responsibility

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Authors: Gashaw Sisay Zenebe

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Northern-Ethiopian conflict that broke out on 04 November 2020 between the central government and TPLF caused destruction beyond imagination in all aspects; millions of people have been killed, including civilians, mainly women, and children. Civilians have been indiscriminately attacked simply because of their ethnic or religious identity. Warrying parties committed serious crimes of international concern opposite to International Humanitarian Law (IHL). A House of People Representatives (HPR) declared that the terrorist Tigrean Defense Force (TDF), encompassing all segments of its people, waged war against North Gondar through human flooding. On Aug 30, 2021, after midnight, TDF launched a surprise attack against Chena People who had been drunk and deep slept due to the annual festivity. Unlike the lowlands, however, ENDF conjoined the local people to fight TDF in these Highland areas. This research examines identity politics and the consequential genocidal massacre of Chena, including its human and physical destructions that occurred as a result of the armed conflict. As such, the study could benefit international entities by helping them develop a better understanding of what happened in Chena and trigger interest in engaging in ensuring the accountability and enforcement of IHL in the future. Preserving fresh evidence will also serve as a starting point on the road to achieving justice either nationally or internationally. To study the Chena case evaluated against IHL rules, a combination of qualitative and doctrinal research methodology has been employed. The study basically follows a unique sampling case study which has used primary data tools such as observation, interview, key-informant interview, FGD, and battle-field notes. To supplement, however, secondary sources, including books, journal articles, domestic laws, international conventions, reports, and media broadcasts, were used to give meaning to what happened on the ground in light of international law. The study proved that the war was taking place to separate Tigray from Ethiopia. While undertaking military operations to achieve this goal, mass killings, genocidal acts, and war crimes were committed over Chena and approximate sites in the Dabat district of North Gondar. Thus, hundreds of people lost their lives to the brutalities of mass killings, hundreds of people were subjected to a forcible disappearance, and tens of thousands of people were forced into displacement. Furthermore, harsh beatings, forced labor, slavery, torture, rape, and gang rape have been reported, and generally, people are subjected to pass cruel, inhuman, and degrading treatment and punishment. Also, what is so unique is that animals were indiscriminately killed completely, making the environment unsafe for human survival because of pollution and bad smells and the consequent diseases such as Cholera, Flu, and Diarrhea. In addition to TDF, ENDF’s shelling has caused destruction to farmers’ houses & claimed lives. According to humanitarian principles, acts that can establish MACs and war crimes were perpetrated. Generally, the war in this direction has shown an absolute disrespect for international law norms.

Keywords: genocide, war crimes, Tigray Defense Force, Chena, IHL

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Authors: Kartik Gupta, Virendra Kumar, Sanjeev Sinha, N. Jagannathan

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Introduction: A significant number of patients having human immunodeficiency virus (HIV) infection show a neurocognitive decline (NCD) ranging from minor cognitive impairment to severe dementia. The possible causes of NCD in HIV-infected patients include brain injury by HIV before cART, neurotoxic viral proteins and metabolic abnormalities. In the present study, we compared the level of NCD in asymptomatic HIV-infected patients with changes in brain metabolites measured by using magnetic resonance spectroscopy (MRS). Methods: 43 HIV-positive patients (30 males and 13 females) coming to ART center of the hospital and HIV-seronegative healthy subjects were recruited for the study. All the participants completed MRI and MRS examination, detailed clinical assessments and a battery of neuropsychological tests. All the MR investigations were carried out at 3.0T MRI scanner (Ingenia/Achieva, Philips, Netherlands). MRI examination protocol included the acquisition of T2-weighted imaging in axial, coronal and sagittal planes, T1-weighted, FLAIR, and DWI images in the axial plane. Patients who showed any apparent lesion on MRI were excluded from the study. T2-weighted images in three orthogonal planes were used to localize the voxel in left frontal lobe white matter (FWM) and left basal ganglia (BG) for single voxel MRS. Single voxel MRS spectra were acquired with a point resolved spectroscopy (PRESS) localization pulse sequence at an echo time (TE) of 35 ms and a repetition time (TR) of 2000 ms with 64 or 128 scans. Automated preprocessing and determination of absolute concentrations of metabolites were estimated using LCModel by water scaling method and the Cramer-Rao lower bounds for all metabolites analyzed in the study were below 15\%. Levels of total N-acetyl aspartate (tNAA), total choline (tCho), glutamate + glutamine (Glx), total creatine (tCr), were measured. Cognition was tested using a battery of tests validated for Indian population. The cognitive domains tested were the memory, attention-information processing, abstraction-executive, simple and complex perceptual motor skills. Z-scores normalized according to age, sex and education standard were used to calculate dysfunction in these individual domains. The NCD was defined as dysfunction with Z-score ≤ 2 in at least two domains. One-way ANOVA was used to compare the difference in brain metabolites between the patients and healthy subjects. Results: NCD was found in 23 (53%) patients. There was no significant difference in age, CD4 count and viral load between the two groups. Maximum impairment was found in the domains of memory and simple motor skills i.e., 19/43 (44%). The prevalence of deficit in attention-information processing, complex perceptual motor skills and abstraction-executive function was 37%, 35%, 33% respectively. Subjects with NCD had a higher level of Glutamate in the Frontal region (8.03 ± 2.30 v/s. 10.26 ± 5.24, p-value 0.001). Conclusion: Among newly diagnosed, ART-naïve retroviral disease patients from India, cognitive decline was found in 53\% patients using tests validated for this population. Those with neurocognitive decline had a significantly higher level of Glutamate in the left frontal region. There was no significant difference in age, CD4 count and viral load at initiation of ART between the two groups.

Keywords: HIV, neurocognitive decline, neurometabolites, magnetic resonance spectroscopy

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Authors: Keivan Davami, Michael Munther, Lloyd Hackel

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The control of properties and material behavior by implementing thermal-mechanical processes is based on mechanical deformation and annealing according to a precise schedule that will produce a unique and stable combination of grain structure, dislocation substructure, texture, and dispersion of precipitated phases. The authors recently developed a thermal-mechanical technique to stabilize the microstructure of additively manufactured nickel-based superalloys even after exposure to high temperatures. However, the mechanism(s) that controls this stability is still under investigation. Laser peening (LP), also called laser shock peening (LSP), is a shock based (50 ns duration) post-processing technique used for extending performance levels and improving service life of critical components by developing deep levels of plastic deformation, thereby generating high density of dislocations and inducing compressive residual stresses in the surface and deep subsurface of components. These compressive residual stresses are usually accompanied with an increase in hardness and enhance the material’s resistance to surface-related failures such as creep, fatigue, contact damage, and stress corrosion cracking. While the LP process enhances the life span and durability of the material, the induced compressive residual stresses relax at high temperatures (>0.5Tm, where Tm is the absolute melting temperature), limiting the applicability of the technology. At temperatures above 0.5Tm, the compressive residual stresses relax, and yield strength begins to drop dramatically. The principal reason is the increasing rate of solid-state diffusion, which affects both the dislocations and the microstructural barriers. Dislocation configurations commonly recover by mechanisms such as climbing and recombining rapidly at high temperatures. Furthermore, precipitates coarsen, and grains grow; virtually all of the available microstructural barriers become ineffective.Our results indicate that by using “cyclic” treatments with sequential LP and annealing steps, the compressive stresses survive, and the microstructure is stable after exposure to temperatures exceeding 0.5Tm for a long period of time. When the laser peening process is combined with annealing, dislocations formed as a result of LPand precipitates formed during annealing have a complex interaction that provides further stability at high temperatures. From a scientific point of view, this research lays the groundwork for studying a variety of physical, materials science, and mechanical engineering concepts. This research could lead to metals operating at higher sustained temperatures enabling improved system efficiencies. The strengthening of metals by a variety of means (alloying, work hardening, and other processes) has been of interest for a wide range of applications. However, the mechanistic understanding of the often complex processes of interactionsbetween dislocations with solute atoms and with precipitates during plastic deformation have largely remained scattered in the literature. In this research, the elucidation of the actual mechanisms involved in the novel cyclic LP/annealing processes as a scientific pursuit is investigated through parallel studies of dislocation theory and the implementation of advanced experimental tools. The results of this research help with the validation of a novel laser processing technique for high temperature applications. This will greatly expand the applications of the laser peening technology originally devised only for temperatures lower than half of the melting temperature.

Keywords: laser shock peening, mechanical properties, indentation, high temperature stability

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Authors: Mian Huang, Chi Ma, Junyu Lin, William Lu

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Introduction: Bone health attracts more attention recently and an intelligent question and answer (QA) chatbot for osteoporosis is helpful for science popularization. With Generative Pretrained Transformer (GPT) technology developing, we build an osteoporosis corpus dataset and then fine-tune LLaMA, a famous open-source GPT foundation large language model(LLM), on our self-constructed osteoporosis corpus. Evaluated by clinical orthopedic experts, our fine-tuned model outperforms vanilla LLaMA on osteoporosis QA task in Chinese. Three-dimensional quantitative computed tomography (QCT) measured bone mineral density (BMD) is considered as more accurate than DXA for BMD measurement in recent years. We develop an automatic Phantom-less QCT(PL-QCT) that is more efficient for BMD measurement since no need of an external phantom for calibration. Combined with LLM on osteoporosis, our PL-QCT provides efficient and accurate BMD measurement for our chatbot users. Material and Methods: We build an osteoporosis corpus containing about 30,000 Chinese literatures whose titles are related to osteoporosis. The whole process is done automatically, including crawling literatures in .pdf format, localizing text/figure/table region by layout segmentation algorithm and recognizing text by OCR algorithm. We train our model by continuous pre-training with Low-rank Adaptation (LoRA, rank=10) technology to adapt LLaMA-7B model to osteoporosis domain, whose basic principle is to mask the next word in the text and make the model predict that word. The loss function is defined as cross-entropy between the predicted and ground-truth word. Experiment is implemented on single NVIDIA A800 GPU for 15 days. Our automatic PL-QCT BMD measurement adopt AI-associated region-of-interest (ROI) generation algorithm for localizing vertebrae-parallel cylinder in cancellous bone. Due to no phantom for BMD calibration, we calculate ROI BMD by CT-BMD of personal muscle and fat. Results & Discussion: Clinical orthopaedic experts are invited to design 5 osteoporosis questions in Chinese, evaluating performance of vanilla LLaMA and our fine-tuned model. Our model outperforms LLaMA on over 80% of these questions, understanding ‘Expert Consensus on Osteoporosis’, ‘QCT for osteoporosis diagnosis’ and ‘Effect of age on osteoporosis’. Detailed results are shown in appendix. Future work may be done by training a larger LLM on the whole orthopaedics with more high-quality domain data, or a multi-modal GPT combining and understanding X-ray and medical text for orthopaedic computer-aided-diagnosis. However, GPT model gives unexpected outputs sometimes, such as repetitive text or seemingly normal but wrong answer (called ‘hallucination’). Even though GPT give correct answers, it cannot be considered as valid clinical diagnoses instead of clinical doctors. The PL-QCT BMD system provided by Bone’s QCT(Bone’s Technology(Shenzhen) Limited) achieves 0.1448mg/cm2(spine) and 0.0002 mg/cm2(hip) mean absolute error(MAE) and linear correlation coefficient R2=0.9970(spine) and R2=0.9991(hip)(compared to QCT-Pro(Mindways)) on 155 patients in three-center clinical trial in Guangzhou, China. Conclusion: This study builds a Chinese osteoporosis corpus and develops a fine-tuned and domain-adapted LLM as well as a PL-QCT BMD measurement system. Our fine-tuned GPT model shows better capability than LLaMA model on most testing questions on osteoporosis. Combined with our PL-QCT BMD system, we are looking forward to providing science popularization and early morning screening for potential osteoporotic patients.

Keywords: GPT, phantom-less QCT, large language model, osteoporosis

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Authors: Magdy I. A. Alshourbagi

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Background: The National Institute for Deafness and Communication Disorders defines idiopathic sudden sensorineural hearing loss as the idiopathic loss of hearing of at least 30 dB across 3 contiguous frequencies occurring within 3 days.The most common clinical presentation involves an individual experiencing a sudden unilateral hearing loss, tinnitus, a sensation of aural fullness and vertigo. The etiologies and pathologies of ISSNHL remain unclear. Several pathophysiological mechanisms have been described including: vascular occlusion, viral infections, labyrinthine membrane breaks, immune associated disease, abnormal cochlear stress response, trauma, abnormal tissue growth, toxins, ototoxic drugs and cochlear membrane damage. The rationale for the use of hyperbaric oxygen to treat ISSHL is supported by an understanding of the high metabolism and paucity of vascularity to the cochlea. The cochlea and the structures within it require a high oxygen supply. The direct vascular supply, particularly to the organ of Corti, is minimal. Tissue oxygenation to the structures within the cochlea occurs via oxygen diffusion from cochlear capillary networks into the perilymph and the cortilymph. . The perilymph is the primary oxygen source for these intracochlear structures. Unfortunately, perilymph oxygen tension is decreased significantly in patients with ISSHL. To achieve a consistent rise of perilymph oxygen content, the arterial-perilymphatic oxygen concentration difference must be extremely high. This can be restored with hyperbaric oxygen therapy. Subject and Methods: A 37 year old man was presented at the clinic with a five days history of muffled hearing and tinnitus of the right ear. Symptoms were sudden onset, with no associated pain, dizziness or otorrhea and no past history of hearing problems or medical illness. Family history was negative. Physical examination was normal. Otologic examination revealed normal tympanic membranes bilaterally, with no evidence of cerumen or middle ear effusion. Tuning fork examination showed positive Rinne test bilaterally but with lateralization of Weber test to the left side, indicating right ear sensorineural hearing loss. Audiometric analysis confirmed sensorineural hearing loss across all frequencies of about 70- dB in the right ear. Routine lab work were all within normal limits. Clinical diagnosis of idiopathic sudden sensorineural hearing loss of the right ear was made and the patient began a medical treatment (corticosteroid, vasodilator and HBO therapy). The recommended treatment profile consists of 100% O2 at 2.5 atmospheres absolute for 60 minutes daily (six days per week) for 40 treatments .The optimal number of HBOT treatments will vary, depending on the severity and duration of symptomatology and the response to treatment. Results: As HBOT is not yet a standard for idiopathic sudden sensorineural hearing loss, it was introduced to this patient as an adjuvant therapy. The HBOT program was scheduled for 40 sessions, we used a 12-seat multi place chamber for the HBOT, which was started at day seven after the hearing loss onset. After the tenth session of HBOT, improvement of both hearing (by audiogram) and tinnitus was obtained in the affected ear (right). Conclusions: In conclusion, HBOT may be used for idiopathic sudden sensorineural hearing loss as an adjuvant therapy. It may promote oxygenation to the inner ear apparatus and revive hearing ability. Patients who fail to respond to oral and intratympanic steroids may benefit from this treatment. Further investigation is warranted, including animal studies to understand the molecular and histopathological aspects of HBOT and randomized control clinical studies.

Keywords: idiopathic sudden sensorineural hearing loss (issnhl), hyperbaric oxygen therapy (hbot), the decibel (db), oxygen (o2)

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Authors: Olga Kononova, Sergey Lyapin

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Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools.

Keywords: contextual knowledge, contextual search, e-library services, frequency-ranked query, paragraph-oriented query, technologies of the contextual knowledge extraction

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

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Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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Authors: Arthur Müller

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The study treats human dimensions of hunting by conducting statistical data analysis and providing decision-making support by examples of good prefectural governance and successful wildlife management, crucial to reduce pest species and sustain a stable hunter population in the future. Therefore it analyzes recent revision of wildlife legislation, reveals differences in administrative management structures, as well as socio-demographic characteristics of hunters in correlation with harvest trends of sika deer and wild boar in 47 prefectures in Japan between 1990 and 2013. In a wider context, Japan’s decentralized license hunting system might take the potential future role of a regional pioneer in East Asia. Consequently, the study contributes to similar issues in premature hunting systems of South Korea and Taiwan. Firstly, a quantitative comparison of seven mainland regions was conducted in Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, and Kyushu. Example prefectures were chosen by a cluster analysis. Shifts, differences, mean values and exponential growth rates between trap and gun hunters, age classes and common occupation types of hunters were statistically exterminated. While western Japan is characterized by high numbers of aged trap-hunters, occupied in agricultural- and forestry, the north-eastern prefectures show higher relative numbers of younger gun-hunters occupied in the field of production and process workers. With the exception of Okinawa island, most hunters in all prefectures are 60 years and older. Hence, unemployed and retired hunters are the fastest growing occupation group. Despite to drastic decrease in hunter population in absolute numbers, Hunting Recruitment Index indicated that all age classes tend to continue their hunting activity over a longer period, above ten years from 2004 to 2013 than during the former decade. Associated with a rapid population increase and distribution of sika deer and wild boar since 1978, a number of harvest from hunting and culling also have been rapidly increasing. Both wild boar hunting and culling is particularly high in western Japan, while sika hunting and culling proofs most successful in Hokkaido, central and western Japan. Since the Wildlife Protection and Proper Hunting Act in 1999 distinct prefectural hunting management authorities with different power, sets apply management approaches under the principles of subsidiarity and guidelines of the Ministry of Environment. Additionally, the Act on Special Measures for Prevention of Damage Related to Agriculture, Forestry, and Fisheries Caused by Wildlife from 2008 supports local hunters in damage prevention measures through subsidies by the Ministry of Agriculture and Forestry, which caused a rise of trap hunting, especially in western Japan. Secondly, prefectural staff in charge of wildlife management in seven regions was contacted. In summary, Hokkaido serves as a role model for dynamic, integrative, adaptive “feedback” management of Ezo sika deer, as well as a diverse network between management organizations, while Hyogo takes active measures to trap-hunt wild boars effectively. Both prefectures take the leadership in institutional performance and capacity. Northern prefectures in Tohoku, Chubu and Kanto region, firstly confronted with the emergence of wild boars and rising sika deer numbers, demand new institution and capacity building, as well as organizational learning.

Keywords: hunting and culling harvest trends, hunter socio-demographics, regional comparison, wildlife management approach

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Authors: Yimer Mohammed Assen, Abiyot Legesse Kura, Engida Esyas Dube, Asebe Regassa Debelo, Girma Kelboro Mensuro, Lete Bekele Gure

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Climate change has posed severe threats to many cultural landscapes of UNESCO world heritage sites recently. The UNESCO State of Conservation (SOC) reports categorized flooding, temperature increment, and drought as threats to cultural landscapes. This study aimed to examine variations and trends of rainfall and temperature extreme events and their threats to the UNESCO-designated Konso Cultural Landscape in southern Ethiopia. The study used dense merged satellite-gauge station rainfall data (1981-2020) with spatial resolution of 4km by 4km and observed maximum and minimum temperature data (1987-2020). Qualitative data were also gathered from cultural leaders, local administrators, and religious leaders using structured interview checklists. The spatial patterns, coefficient of variation, standardized anomalies, trends, and magnitude of change of rainfall and temperature extreme events both at annual and seasonal levels were computed using the Mann-Kendall trend test and Sen’s slope estimator under the CDT package. The standard precipitation index (SPI) was also used to calculate drought severity, frequency, and trend maps. The data gathered from key informant interviews and focus group discussions were coded and analyzed thematically to complement statistical findings. Thematic areas that explain the impacts of extreme events on the cultural landscape were chosen for coding. The thematic analysis was conducted using Nvivo software. The findings revealed that rainfall was highly variable and unpredictable, resulting in extreme drought and flood. There were significant (P<0.05) increasing trends of heavy rainfall (R10mm and R20mm) and the total amount of rain on wet days (PRCPTOT), which might have resulted in flooding. The study also confirmed that absolute temperature extreme indices (TXx, TXn, and TNx) and the percentile-based temperature extreme indices (TX90p, TN90p, TX10p, and TN10P) showed significant (P<0.05) increasing trends which are signals for warming of the study area. The results revealed that the frequency as well as the severity of drought at 3-months (katana/hageya seasons) was more pronounced than the 12-months (annual) time scale. The highest number of droughts in 100 years is projected at a 3-months timescale across the study area. The findings also showed that frequent drought has led to loss of grasses which are used for making traditional individual houses and multipurpose communal houses (pafta), food insecurity, migration, loss of biodiversity, and commodification of stones from terrace. On the other hand, the increasing trends of rainfall extreme indices resulted in destruction of terraces, soil erosion, loss of life and damage of properties. The study shows that a persistent decline in farmland productivity, due to erratic and extreme rainfall and frequent drought occurrences, forced the local people to participate in non-farm activities and retreat from daily preservation and management of their landscape. Overall, the increasing rainfall and temperature extremes coupled with prevalence of drought are thought to have an impact on the sustainability of cultural landscape through disrupting the ecosystem services and livelihood of the community. Therefore, more localized adaptation and mitigation strategies to the changing climate are needed to maintain the sustainability of Konso cultural landscapes as a global cultural treasure and to strengthen the resilience of smallholder farmers.

Keywords: adaptation, cultural landscape, drought, extremes indices

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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