AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, H3K9me3 and H3K27me3 large domains, HiC compartments and lamina associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNAs) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

Soil water infiltration is an important component of the hydrological cycle and it is best evaluated when the raindrop is impacted on the ground surface. For this reason, it is affected by changes in land use and occupation, and by the characteristics and physical-hydric properties of the soil. This study aimed to evaluate soil water infiltration in areas occupied with annual crops (soybean and corn) and pastures, in two watersheds of the Teles Pires River-MT, using simulated rainfall, physical models and principal component analysis. Infiltration rates were evaluated from simulated rainfall with an average intensity of 75 mm h-1, with 4 repetitions per region (upper, middle and lower) of the hydrographic sub-basins of the Caiabi and Renato rivers, and soil use with cover, without cover and disturbed. Soil tillage provided higher water infiltration rates into the soil, especially in pasture areas in the two hydrographic sub-basins. There were significant adjustments of the mathematical models to the infiltration rate data for all land use and land cover conditions. The soil attributes that most interfered with the infiltration rate were microporosity, soil density and total porosity in the crop areas of the middle Caiabi, and microporosity, clay content, total porosity and silt content in the areas farming at the source of the Renato River. The Horton and Philip models presented the best adjustments in the hydrographic sub-basins of the Renato and Caiabi and Renato rivers, being recommended for estimating the rate of water infiltration into the soil in different uses, coverages, and regions.

This study analyzed the CO2 capture potential for Parque Natural Regional Serrania de las Quinchas buffer area in Colombia. For this purpose, multitemporal analysis for land covering for years 1989, 2000, 2006, 2011, 2017 and 2021 was made using the Normalized Difference Vegetation Index (NDVI), and Corine Land Cover (CLC) methodology. In the same way, Aboveground Biomass (AGB) was measured for representative parcels by measuring tree’s diameters and heights and applying the adequate allometric models; carbon content in soils was measured too. Results show that dense forest has grown since 1989 and the carbon stock in all the vegetation covers increased by 14,3% from 1989 until 2021, using literature data for tropical humid forests. If measured AGB values obtained in this study are used, the carbon stock average is 3 times this value. Measured carbon average content in AGB was 374,3 Ton C/Ha, well above the average for the content reported for humid tropical forests in Colombia; carbon content in soils was 897,19 Ton/Ha, 2,4 times that found in the AGB. In total, an average of 1271,49 Ton C/Ha (AGB+ soil) was found and a pattern of dense forest recovery was found, but in a more dispersed way than in the past. A very interesting potential for existing forests recovery was found for this area. Strategies for this include the development of sustainable practices, land use management, everything pursuing biodiversity preservation and the participation and leading of the local communities.

The Gulf of Suez is a sedimentary rift-basin with thick evaporites and complex structural settings that presents challenges to achieving quality imaging based on seismic reflection data. Such subsurface settings and the lack of 3D seismic data poses challenges in building reservoir models to guide hydrocarbon exploration and development, especially in southern parts the Gulf of Suez. This study attempts to overcome the challenges of both 2D seismic interpretation and building a static reservoir model of two Miocene reservoir formations, Ras Ghara Field, Southern Gulf of Suez. Integration of well log data analysis of nine wells and regional-tectonics cognizant 2D seismic interpretation of nineteen 2D seismic lines enabled a reliable 3D static reservoir model. The Miocene reservoir characterization reveals heterogeneity in lithofacies distribution, with thick evaporite sequences causing seismic reflection distortions. Significant hydrocarbon accumulations are associated with horst fault blocks. The Miocene reservoirs demonstrate effective porosity of 14-33%, water saturation of 16-65%, and hydrocarbon saturation of 35-84%. Two new prospects are identified based on structure, facies distribution, and petrophysical results, with estimated original oil in place (OOIP) of 2,002,404 stock tank barrels (STB) and 1,175,580 STB for the Kareem Formation, and 6,302,916 STB and 2,841,894 STB for the Rudeis Formation.

Abstract Background: Cluster randomized controlled trials are increasingly becoming popular in public health research interventions and health services research in primary health care design and use in low and middle-income countries. However, the majority of researchers in these trials did not account correctly for the effect of confounders and clustering in the design and analysis stages. Objectives: This article aimed to review the main implications of implementing a cluster randomized controlled trials design in public health research and emphasize the practical application of proper analytical methods and the performance of different statistical models. Methods: The practical application of various analytical methods is validated via the utilization of experimental data from a public health research case study. This article also demonstrated the performance of hierarchical models and made comparisons in terms of empirically precise effect size estimates. Results: Three hierarchical statistical models were provided with highly varied effect sizes during the analysis of the intervention effect on outcome, such as model 1 (AOR: 14.32; 95% CI: 3.75–54.68), model 2 (ARR: 3.52; 95% CI: 2.16–5.77), and model 3 (ARR: 4.79; 95% CI: 2.69–5.52).Conclusion: Inaccurate results and potentially misleading conclusions can arise from improper cluster randomized controlled trial designs and analyses. This article has shown that real-world data can be adjusted for the effects of confounders and clustering, and we advocate for the routine use of suitable design and analytical methods. This article concludes that researchers should be careful when selecting hierarchical models to analyze cluster randomized controlled trials.

The reduction of Ce cations in CeO2 can be enhanced by its partial substitution by Fe cations. The enhanced reduction of Ce cations results in a considerable increase in the reaction rates for thermal water splitting reaction when compared to CeO2 alone. This mixed oxide has a smaller crystallite size when compared to CeO2 in addition to a smaller lattice size. In this work, two Fe-substituted Ce oxides are studied (Ce0.95Fe0.05O2 and Ce0.75Fe0.25O2) by core and valence level spectroscopy in their as-prepared and Ar-ions sputtered states. Ar-ions sputtering substantially increases Ce4f lines at about 1.5 eV below the Fermi level. In addition, it is found that the XPS Ce5p/O2s ratio is sensitive to the degree of reduction, most likely due to a higher charge transfer from the oxygen to Ce ions upon reduction. Quantitatively it is also found that XPS Ce3d of the fraction of Ce3+ (uo, u’ and vo, v’) formed upon Ar-ions sputtering and the ratio Ce5p/O2s lines are higher for reduced Ce0.95Fe0.05O2 than for reduced Ce0.75Fe0.25O2. XPS Fe2p showed however no preferential increase for Fe3+ reduction to Fe0 with increasing time for both oxides. Since water splitting was higher on Ce0.95Fe0.05O2 when compared to Ce0.75Fe0.25O2, it is inferred that the reaction centers for the thermal water splitting to hydrogen are the reduced Ce cations and not the reduced Fe cations. These reduced Ce cations can be tracked by their XPS Ce5p/O2s ratio in addition to the common XPS Ce3d lines.

Recent years have experienced an increase in construction and demolition waste (C&DW), which is the most significant solid waste stream. C&DW is contributing to exceeding all planetary boundaries and presents a range of other issues. In order to better understand the existing research on C&DW, a global bibliographic analysis was undertaken through seven groups of keyword searches and the results visualised using VOCviewer. The results show that C&DW receives only a modest research attention compared to other areas of waste, and this is despite an exponential increase in C&DW research since 2016. The analyses also show that concrete is the most researched material in terms of C&DW, and that reuse, recycling, and circular economy are so far attracting only proportionally modest research attention. This signals a need for further acceleration of the C&DW research, and specifically for more research on reuse, recycling, and circular economy for materials other than concrete.

Today, there is a time series analysis that has gained significant popularity across various industries to provide effective solutions for any specific challenges. Artificial intelligence logic is generally used to solve time-series problems with the help of various frameworks that are available for the development of AI-based applications. Currently, there is a significant framework in Python that is driven by its comprehensive libraries and their practical implementations. Time series analysis can be built with the help of some specific models that give results to differentiate the efficiency and domains of great work. This paper focuses on three diverse libraries of Python to predict the Gulf stock exchange market. These libraries are prediction models that are helpful in predicting future values. These models utilized include SARIMA (Seasonal Auto-Regressive Integrated Moving Average), Neural Prophet Model, and Fb Prophet model. Each model is trained using the provided datasets, which are followed by evaluation parameters such as MSE, RMSE, recall, MDAPE, accuracy, precision, etc. The main purpose of using these three models for stock market prediction is to find the better-suited models. In the past few decades, the stock market industry has seemed to have promising growth after 2022 onwards. This paper will help us make a significant decision about model usage for addressing such complex challenges. Especially, the research findings indicate that all three models demonstrate in different domains. SARIMA can perform well in overall prediction tasks, while the Prophet model works great with smaller datasets that contain seasonality trends.

The Mahuagou gold deposit is among the most important gold deposits in the core of Huangling Anticline. However, the geochemical exploration on the surface of the mining area presents challenges due to the thin overburde.This paper focuses on the overburden soil of Fengxiangshugou (FXS) – Mahuanggou (MHG) section as the research object, utilizes chemical form analysis of gold, soil halogen survey, and heat-released mercury survey to determine the key deep-penetrating geochemical methods for mining area. The results indicated that Si and Al components of samples exhibit minimal variation, suggesting that drift loads did not influence the overburden soil. Based on the systematic clustering, As, Sb, Mo, Bi, W and Hg emerge as ore-body or ore-belt front elements of hydrothermal gold deposits. In the study area, the predominant chemical form of gold in soil is the strong organic bond. Compared to the total amount, strong organic bound gold and heat-released mercury show higher anomaly contrasts, making them crucial indicators of faults, intrusions and hidden ore bodies. Consequently, chemical form analysis of gold and heat-released mercury surveys can enhance the anomaly contrast, proving beneficial for geochemical prospecting for weak anomalies in this area.

In this study, multi-layer ceramic capacitors (MLCCs) detached from lighting sector's waste electrical-electronic equipment (WEEE) are characterised for the presence of rare earth elements (REEs) and precious metals (PMs). Their digestion was carried out with HNO3 & aqua regia on a heating plate and characterised with the use of inductively coupled plasma optical emission spectroscopy (ICP-OES) & scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). The contents of REEs and PMs are 0.84 wt% and 0.60 wt%, respectively, and create an economic stored value in MLCCs that is essentially defined by PMs of 98.67% and by Pd 78.37%. The content of the main elements is: Nd 0.366 wt%, Y 0.220 wt%, Dy 0.131 wt%, Ag 0.467 wt%, and Pd 0.105 wt%. The results confirm the need of selective removal and separate recycling process of MLCCs from WEEE drivers.

Criminal investigations aimed to track the route walked by Missing Persons and Fugitives (MPFs) usually involve Intelligence analysts, military planners, experts in mobile forensics, traditional investigative methods, and sniffer dog handlers. Notwithstanding, when MPFs are devoid of any technological device and move in uninhabited rural areas devoid of tele cameras and densely covered by vegetation, tracking backwards the route walked by MPFs may be a much more arduous task. In such complex cases, a very efficient approach may consist in comparing the geological traces found on the MPFs with soils and plants exposed in the event scenes. In particular, the search for peculiar or rare particles and aggregates may strengthen the weight of the geological evidence comparisons. A match of mineralogical, textural, and botanical data may demonstrate the provenance of the traces from the soil of a specific site, linking in this way the MPFs to the scene of events. Based on the above, the present paper reports geological and botanical determinations accomplished for a “mediatic” casework. Results allowed to ascertain a general high degree of compatibility among traces collected on the MPFs and on the soil from the scene of events. The most significant positive matches, based on the finding of a ten of peculiar and rare particles and assemblages, allowed reconstructing a route about 1.1 km long, as the crow flies, on the event site. Notwithstanding this procedure was extremely time consuming and available only in a backwards reconstruction linked to the MPFs’ findings, it was of uttermost importance in strengthen the inferences proposed, where other methods could not provide any information.

The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present cable-driven reconfigurable robot kinematics and statics. We studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. Likewise, we propose a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporate a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We reconfigure the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Likewise, we also computed the workspace to show that the reconfigurable design fits several sizes. The data used is from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators.

Droughts are among the major natural hazards that are spreading to many parts of the world with huge multi-dimensional impacts. For Continental Croatia an extensive analysis of drought phenomenon is presented based on meteorological E-OBS gridded dataset (0.25° x 0.25°), within the period of 1950 to 2022. The drought events were characterized by the Standardized Precipita-tion Evapotranspiration Index (SPEI) applied to different time-scales (6 and 12 months) in order to describe the subannual and annual variability of drought. The spatiotemporal patterns of drought are obtained through principal component analysis (PCA) and K-means clustering (KMC) applied to the SPEI field. An areal drought evolution analysis and the changes in the frequency of occur-rence of the periods under drought conditions were achieved by using a kernel occurrence rate es-timator (KORE). The Modified Mann-Kendall (MMK) test coupled with the Sen’s slope estimator test are applied to the SPEI series in order to quantify the drought trends throughout the country. According to drought events history and considering the different morphoclimatic characteristics of the study area the results showed that Croatia could be divided into three different and spatial-ly well-defined regions with specific temporal and spatial characteristics of droughts (Central North, Eastern and Southern regions). It is shown that there is a manifest increase in the percent-age of area affected by drought as well as in the yearly drought occurrences rates in both Central North and Eastern regions and an evident decrease in the Southern region for both 6 and 12 month SPEI time-scales. In the observation of the drought temporal characteristics, it was found that downward trends expressing increasing drought severities were strongly significant in North and Eastern regions while a few significant upward trends were seen in Southern region. From this study, it is possible to obtain a broader view of the historical behaviour of droughts in Croatia with results providing useful support for drought risk assessment and decision-making processes.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

Disrupted lipid metabolism is a characteristic of gliomas. This study utilizes an ultrastructural approach to characterize the prevalence and distribution of lipids within gliomas. This study made use of tissue from IDH1 wild type (IDH1-wt) glioblastoma (n=18) and IDH1 mutant (IDH1-mt) astrocytoma (n=12) tumors. We uncover a prevalent and intriguing surplus of lipids. The bulk of the lipids manifested as sizable cytoplasmic inclusions and extracellular deposits in the tumor microenvironment (TME); in some tumors the lipids were stored in the classical mem-braneless spheroidal lipid droplets (LDs). Frequently, lipids accumulated inside mitochondria, suggesting possible dysfunction of the beta-oxidation pathway. Additionally, the tumor vascula-ture are shown to have lipid deposits in their lumen and vessel walls; this lipid could have shifted in from the tumor microenvironment or have been produced by the vessel-invading tumor cells. Overproduction of lipids by the tumor is suspected to be due to a combination of two processes: i) dysfunctional oxidative phosphorylation in mitochondria that causes metabolic backup and ac-cumulation of acetyl-CoA, as well as ii) the overproduction of pyruvate due to the tumor cell reli-ance on overactive glycolysis. Tumor cells use lipids for structural components but cannot use them for energy if their oxidative phosphorylation is dysfunctional, which seems to be the case in the tumor cells we examined. This lipid-rich environment, however, offers tumor cells certain advantages: protection from the immune system, non-lipophilic drugs, and oxidative stress. Our research highlights the significant presence of lipid overproduction in both IDH1-wild type glio-blastoma and IDH1-mutant astrocytoma tumors, leading to diverse lipid accumulations in var-ious cellular compartments and structures. This lipid excess stems from disrupted beta-oxidation and dysfunctional oxidative phosphorylation pathways. The implications of this lipid-driven environment include structural support for the tumor cells and protection against immune re-sponses, non-lipophilic drugs, and free radicals.

Cyanobacteria, microorganisms performing oxygenic photosynthesis, must adapt their metabolic processes to environmental challenges such as day and night changes. PipX, a unique regulatory protein from cyanobacteria provides a mechanistic link between signalling protein PII, a widely conserved (in bacteria and plants) transducer of carbon/nitrogen/energy richness, and the transcriptional regulator NtcA, which controls a large regulon involved in nitrogen assimilation. PipX is also involved in translational regulation by interaction with the ribosome-assembly GTPase EngA. However, increases of the PipX/PII ratio are toxic, presumably by abnormally increased binding of PipX to other partner(s). Here we present mutational and structural analyses of reported PipX-PII and PipX-NtcA complexes, leading to identification of single amino acid changes that decrease or abolish PipX toxicity. Four out of 11 mutations decreasing toxicity did not decrease PipX levels, suggesting that the targeted residues (F12, D23, L36, R54) provide toxicity determinants. In addition, one of those four mutations (D23A) argued against the over-activation of NtcA as the cause of PipX toxicity. Since most mutations at residues contacting PII decreased PipX levels, PipX stability would depend on its ability to bind to PII, a conclusion supported by the light-induced decrease in PipX levels observed in Synechococcus elongatus PCC7942.

Conjugative transposons in Gram-negative bacteria have a significant role in the dissemination of antibiotic-resistance-conferring genes between bacteria. This study aims to genomically characterize plasmids and conjugative transposons carrying integrons in clinical isolates of Klebsiella pneumoniae. Genetic composition of conjugative transposons and phenotypic assessment of 50 multidrug-resistant K. pneumoniae isolates from Tertiary-care Hospital (SQUH), Muscat, Oman was investigated. Horizontal transferability was investigated by filter- mating conjugation experiments. Whole genome sequencing (WGS) was performed to determine the sequence type (ST), acquired resistome and plasmidome of integron-carrying strains. Class 1 integron was detected in 96% of isolates and among integron-positive isolates, 18 stains contained variable regions. Horizontal transferability by conjugation confirmed the successful transfer of integrons between cells and WGS confirmed their presence in conjugative plasmids. Dihydrofolate reductase (dfrA14) was the most prevalent (34.8%) gene cassettes in class 1 integrons. MLST analysis detected predominantly ST-231 and ST-395. BlaOXA-232 and blaCTX-M-15 were the most frequently detected carbapenems and beta-lactamases in sequenced isolates. This study highlighted the high transmissibility of MDR-conferring conjugative plasmids in clinical isolates of K. pneumoniae. Therefore, the wise use of antibiotics and the adherence to effective infection control measures are necessary to limit further dissemination of multidrug-resistant organisms.

The objective of this research is to analyze metal elements like Na (sodium), Mg (magnesium), Al (aluminum), Si (silicon), Pb (lead), K (potassium), Ca (calcium), and Fe (iron) found in dust particles within two distinct areas from which the sampling has been done. First sampling was taken from the road verge of a highly trafficked road section, while the second sampling was done in a residential garden area 90 m away from the road. Several metal elements have been detected, with a high difference for Silicon Si, which present higher concentrations in the dust sampled from the road verge area. Pb has only been detected in the samples taken from the road verge, which could be explained by residual remnants from old lead gasoline, and wheel weights. Additionally, airborne particulate matter (PM) concentrations have been measured, during the same episode, in comparison between the road verge and the garden area, presenting a substantial difference in the concentration levels, deducing that dense vegetation is protecting and blocking a majority of airborne PM. A literature highlight of health effects of different metal elements and PM concentrations is presented.

Low-intensity noisy galvanic vestibular stimulation (nGVS) can improve static and dynamic postural deficits in patients with bilateral vestibular loss (BVL). In this study, we aimed to explore the neurophysiological and neuroanatomical substrates underlying nGVS treatment effects in a rat model of BVL. Regional brain activation patterns and behavioral responses to repeated 30-min nGVS treatment in comparison to sham stimulation were investigated by serial whole-brain [18F]-FDG-PET measurements and quantitative locomotor assessments before and at 9 consecutive time points up to 60 days after chemical bilateral labyrinthectomy (BL). [18F]-FDG-PET revealed a broad nGVS-induced modulation on regional brain activation patterns encompassing biologically plausible brain networks in the brainstem, cerebellum, multisensory cortex and basal ganglia during the entire observation period post-BL. nGVS broadly reversed brain activity adaptions occurring in the natural course post-BL. Parallel behavioral locomotor assessment demonstrated a beneficial treatment effect of nGVS on sensory-ataxic gait alterations, particularly in the early stage of post-BL recovery. Stimulation-induced locomotor improvements were finally linked to nGVS brain activity responses in the brainstem, hemispheric motor and limbic networks. In conclusion, combined [18F]-FDG-PET and locomotor analysis discloses the potential neurophysiological and neuroanatomical substrates that mediate previously observed therapeutic nGVS effects on postural deficits in patients with BVL.

The degradation of Proton-exchange membrane fuel cell (PEMFC) gasket materials is crucial in electric vehicles as it can cause hazardous hydrogen fuel leaks, which are usually due to high temperatures, pressures, and hydrogen fuel exposure. Degradation of gasket materials in PEMFC presents a critical concern for electric vehicle safety due to potential hydrogen fuel leaks. This study utilizes finite element analysis (FEA) to assess the suitability of gasket materials for PEMFC applications, focusing on aging and tensile conditions. The dual degradation framework, incorporating contact pressure and von Mises stress, is employed to evaluate Liquid Silicon Rubber (LSR) and Ethylene Propylene Diene Monomer (EPDM) materials. Under aging techniques, the Yeoh model exhibits the least Mean Absolute Percentage Error (MAPE) and computational cost of 0.27 seconds, while the Ogden model records the highest computational cost of 0.89 seconds. In evaluating MAE, Root Mean Square Error (RMSE), and R-squared metrics, LSR and EPDM materials demonstrate respective averages of 0.25%, 0.275%, 0.945%, and 0.815%, 0.685%, 0.77%. Tensile testing (Uniaxial) reveals RMSE and MAE values of 0.30%, 0.40%, and 0.50%, 0.40%, respectively. FEA proves instrumental in identifying suitable gasket materials for PEMFC applications. LSR emerges as the superior choice, demonstrating enhanced FEA modelling performance under aging and tensile conditions. These findings contribute valuable insights to the design and development of improved gasket materials, bolstering the safety and reliability of electric vehicles.

Blood gas analysis plays a central role in modern medicine. Advances in technology have expanded the range of available parameters and increased the complexity of their interpretation. By applying user-centered design principles, it is possible to reduce the cognitive load associated with interpreting blood gas analysis. In this international, multicenter study, we explored anesthesiologists' perspectives on Visual Blood, a novel visualization technique for presenting blood gas analysis results. We conducted interviews with participants following two computer-based simulation studies: the first utilizing Virtual Reality (VR) (50 participants) and the second without VR (70 participants). Employing the template approach, we identified key themes in the interview responses and formulated six statements, which were rated using Likert scales: from 1 (strongly disagree) to 5 (strongly agree) in an online questionnaire. The most frequently mentioned theme was the positive usability features of Visual Blood. The online survey revealed that participants found Visual Blood to be an intuitive method for interpreting blood gas analysis (median 4, IQR 4-4, p<0.001). Participants noted that minimal training was required to effectively learn how to interpret Visual Blood (median 4, IQR 4-4, p<0.001). However, adjustments are necessary to reduce visual overload (median 4, IQR 2-4, p<0.001). Overall, Visual Blood received a favorable response. The strengths and weaknesses derived from these data will help optimize future versions of Visual Blood to improve the presentation of blood gas analysis results.

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 to August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, a brief analysis of specific features of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis.

In this study, we report the first outbreak of highly pathogenic avian influenza (HPAI) A H5N8, clade 2.3.4.4b in Kosovo on May 19, 2021. The outbreak consisted of three temporal phases: May–June 2021, September–November 2021, and January–May 2022. In total, 32 backyard and 10 commercial holdings tested positive for the virus, affecting 179,198 poultry. Interestingly, the third and last phase of the outbreak coincided with the massive H5N1 clade 2.3.4.4b epidemic in Europe. Phylogenetic analyses of 28 viral strains from Kosovo revealed that they were closely related to the H5N8 clade 2.3.4.4.b viruses that have been circulating in Albania, Bulgaria, Croatia, Hungary, and Russia in early 2021. Whole genome sequencing of the 25 and partial sequencing of 3 H5N8 viruses from Kosovo showed high nucleotide identity, forming a distinctive cluster and suggesting a single introduction. The results of the network analysis were in accordance with the three epidemic waves and suggested that the viral diffusion could have been caused by secondary spreads among farms and/or different introductions of the same virus from wild birds. The persistent circulation of the same virus over a one-year period highlights the potential risk of the virus becoming endemic, especially in settings with non-adequate biosecurity.

Objective: To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design: Systematic umbrella review of existing meta-analyses. Data Sources: MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manually searching reference lists from 2009 to June 2023. Inclusion Criteria: Systematic reviews and meta-analyses of cohort, case-control, and/or cross-sectional study designs that evaluated the associations between exposure to ultra-processed foods and adverse health outcomes in humans across the lifespan. Results: The search identified 45 unique pooled analyses, including 13 dose-response and 32 non-dose-response associations (n=9,888,373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health outcome domains spanning cardiometabolic, gastrointestinal, and respiratory conditions, cancer, mental health, and mortality. Based on a pre-specified evidence classification criteria, convincing evidence (Class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease-related mortality (RR: 1.50; 95%CIs: 1.37 to 1.63), type two diabetes (dose-response RR: 1.12; 95%CIs: 1.11 to 1.13), and depressive outcomes (HR: 1.21; 95%CIs: 1.16 to 1.28), as well as higher risks of prevalent anxiety outcomes (OR: 1.48; 95%CIs: 1.37 to 1.59) and combined common mental disorder outcomes (OR: 1.53; 95%CIs: 1.43 to 1.63). Highly suggestive (Class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all-cause mortality (RR: 1.21; 95%CIs: 1.15 to 1.27), heart disease-related mortality (dose-response HR: 1.66; 95%CIs: 1.51 to 1.84), and type two diabetes (OR: 1.40; 95%CIs: 1.23 to 1.59), together with higher risks of prevalent obesity (OR: 1.55; 95%CIs: 1.36 to 1.77), adverse sleep-related outcomes (OR: 1.41; 95%CIs: 1.24 to 1.60), and wheezing (RR: 1.40; 95%CIs: 1.27 to 1.55). Out of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (Class III-IV) and 13 were graded as no evidence (Class V). Using the GRADE framework, 22 pooled analyses were rated as "Low" quality, with 19 rated as "Very Low” and four rated as "Moderate" quality. Conclusions: Higher exposure to ultra-processed food was associated with a greater risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to evaluate the effectiveness of using population-based measures to target and reduce dietary exposure to ultra-processed foods for improved human health. Systematic Review Registration: PROSPERO CRD42023412732.

Background: In this secondary analysis of the Supporting the Modification of Lifestyle In Lowered Emotional States (SMILES) randomised controlled trial, we investigated if the beneficial effects of a dietary intervention on clinical depression were driven, in part, by reducing the consumption of foods classified as ultra-processed.Methods: The SMILES trial enrolled 67 adults with major depressive disorder, randomly assigning them to either a 12-week modified Mediterranean dietary intervention or a social support control. Our analysis included 44 participants with non-missing dietary data and at least one valid Montgomery–Åsberg Depression Rating Scale (MADRS) assessment at 12 weeks of follow-up. The Nova food classification system was used to estimate the proportion of ultra-processed foods in the overall diet (percentage of grams) based on data from seven-day food diaries. We fitted linear regression models under blinded conditions to determine whether ultra-processed food intake-change from baseline to 12 weeks moderated the dietary intervention effects on depressive symptoms. We estimated mean differences in depressive symptoms along with ninety-five per cent confidence intervals (95%CIs).Results: For participants in the dietary intervention, there was an additional 2.5-point improvement in MADRS scores for each 10% reduction in the dietary share of ultra-processed foods compared to participants in the control group (between-group mean differences in depressive symptoms: −2.46, 95%CIs −4.71 to −0.20, p=0.039, η _p ² of 0.10).Conclusions: These preliminary findings suggest that the therapeutic benefit of a dietary intervention for depression may stem, at least in part, from reductions in the dietary share of ultra-processed foods.

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one’s quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA Adverse Event Reporting System(FAERS) Data maintained in the United States. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. Principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

In the Formula Student Racing Car, the anti-deformation ability of the frame will affect the four-wheel positioning parameters of the racing car, and then affect the handling and stability performance of the racing car. The quality of the frame directly determines the power and economy of the racing car, so the performance of the frame is very important to the performance of the race. In order to improve the performance of the frame and reduce the total weight of the frame, the research on the lightweight design of the frame is particularly important. In this paper, based on the variable density method, the global topology optimization of the frame is carried out to obtain the distribution of the frame material, realize the efficient use of the material, and improve the torsional stiffness of the frame. Based on the adjoint variable method, the sensitivity analysis of the frame is carried out. According to the sensitivity analysis results, the size of the frame is optimized. The total mass of the frame is reduced by 12.8 %, and the performance of the maximum displacement and maximum stress is improved. The lightweight design of the frame is realized as a whole.

This paper presents preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. Building upon previous studies that measured the physical synchronicity of team members by tracking their body movements and measuring the difference in arm, leg, and head movements, we introduce a novel metric termed "team entanglement". We employ facial expression recognition to evaluate the musicians’ collective emotions. Through correlation and regression analysis, we establish that higher levels of synchronized body and head movements correspond to lower levels of disgust, anger, sadness and higher levels of joy among the musicians. Furthermore, we utilize a Convolutional Neural Network (CNN) based deep learning model to predict the collective emotions of the musicians. This model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.

The Global Burden of Disease Study (GBD 2019 Diseases and Injuries Collaborators) found that diabetes significantly increases the overall burden of disease, leading to a 24.4% increase in disability-adjusted life years. Persistently high glucose levels in diabetes can cause structural and functional changes in proteins throughout the body, and the accumulation of protein aggregates in the brain is associated with the progression of Alzheimer's Disease (AD).To address this burden of type 2 diabetes mellitus (T2DM), a combined aerobic and resistance exercise program was developed based on the recommendations of the American College of Sports Medicine. The prospectively registered clinical trials (https://www.clinicaltrials.gov/ct2/show/NCT04626453, https://www.clinicaltrials.gov/ct2/show/NCT04812288) involved two groups: an Intervention group of older sedentary adults with T2DM and a Control group of healthy older adults who could be either active or sedentary. The completion rate for the 2-month exercise program was high, with participants completing on an average of 89.14% of the exercise sessions. This indicated that the program was practical, feasible, and well-tolerated, even during the COVID pandemic. It was also safe, requiring minimal equipment and no supervision. The exercise instructions were easy to understand, making them suitable for older adults with cognitive decline.The near-infrared spectroscopy (NIRS) based brain and muscle oxygenation study provided evidence on brain overactivation among older adults with T2DM, supporting the compensatory theory. It also demonstrated that the 2-month combined exercise intervention effectively reduced brain overactivation and contributed to improved cognitive function. Operational modal analysis showed an exercise-related effect on the very low-frequency hemodynamic oscillations cluster, which may be associated with better vascular muscle and/or perivascular neurogenic regulation. Furthermore, we personalized the exercise duration and interval based on muscle oxygenation during physical tasks, leading to improvements in muscle oxidative capacity within just two months. This finding has practical implications for physical therapists, as they can target muscle oxygenation changes during physical tests to prescribe appropriate exercise doses for enhancing physical performance.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

Variable Angle Tow (VAT) laminates offer a promising alternative to classical straight fiber composites in terms of design and performance. However, analyzing these structures can be more complex due to the introduction of new design variables. Carrera's Unified Formulation (CUF) has been successful in previous works for buckling, vibrational, and stress analysis of VAT plates. Typically, one-dimensional (1D) and two-dimensional (2D) CUF models are used, with a linear law describing the fiber orientation variation in the main plane of the structure. The objective of this article is to expand the CUF 2D plate finite elements family to perform free vibration analysis of composite laminated plate structures with curvilinear fibers. The primary contribution is the application of Reissner's Mixed Variational Theorem (RMVT) to a CUF finite element model. The Principle of Virtual Displacements (PVD) and RMVT are both used as variational statements for the study of monolayer and multilayer VAT plates dynamic behavior. The proposed approach is compared to Abaqus three-dimensional (3D) reference solutions, classical theories and literature results to investigate the effectiveness of the developed models. Results demonstrate that mixed theories provide the best approximation of the reference solution in all cases.

Cervical cancer is a significant global health concern, with human papillomavirus (HPV) infection being a crucial risk factor. Photodynamic therapy (PDT) is an attractive, minimally invasive treatment for HPV-related cervical lesions, which uses photosensitizers and light to selectively de-stroy abnormal cells. We aim to provide an overview of the various sorts of dyes that are utilized in PDT for decreasing the incidence and mortality of cervical cancer. Besides, the article discusses ongoing clinical trials for PDT in low-grade squamous intraepithelial neoplasia (LSIL) and high-grade squamous intraepithelial lesions (HSIL), as well as preclinical approaches for PDT in cervical cancer using various dyes. Moreover, it highlights potential dyes for PDT, examine them in in-silico condition, their pros and cons and also the solutions for enhancing their anticancer com-patibility. We also display that PDT is a promising therapeutic strategy for the diagnosis and treatment of HPV-associated cervical lesions. Moreover, it shows that using different classes of dyes improves the anticancer effects of PDT. Finally, among all dyes which are used in PDT, Fullerene demonstrated high tendency to over expressed receptors in cervical cancer cells and seems to be a proper candidate for be used in PDT more than before, but further research is necessary to assess its long-term efficacy and safety.

Analysing and understanding donor behaviour in Non-profit Organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an Artificial Intelligence enabled Decision Support System (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a Design Science Research approach to evaluate an AI-enabled DSS's initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment through conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The results of analysis based on the interviews provide insightful information not only about the proposed conceptual design for an AI-enabled DSS, but also about what is required to analyse donor behaviour NPOs using DSS. Evaluating the conceptual design results recommend efficiency, effectiveness, flexibility, and useability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSS for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such analysis on donors who donate money or volunteer time for NPOs.

The growing integration of renewable energy sources (RES) into the power system causes congestion to occur more frequently. In order to reduce congestion in the short term and to make the utilization of the power system more efficient in the long term, power flow control (PFC) in the transmission system has been proposed. However, exemplary studies show that congestion will increase also in the distribution system if the transmission system is expanded. For this reason, the potential of PFC to reduce congestion in a model of a real 110 kV distribution system is investigated. Several Unified Power Flow Controller (UPFC) devices are optimized in terms of their number and placement in the power system, their size, control parameters, and costs, by using a Parallel Tempering approach as well as a greedy algorithm. Two optimization variants are considered, one reducing the number of degrees of freedom by integrating system knowledge while the other is not. We find that near a critical grid state and disregarding costs, PFC can reduce congestion significantly (99.13 %). When costs of the UPFCs are taken into account, PFC can reduce congestion by 73.2 %. A basic economic analysis of the costs reveals that the usage of UPFCs is profitable. Furthermore, it is found that the reduction of the solution space of the optimization problem leads to better results faster and that, contrary to expectations, the optimization problem is simple to solve. The developed methods allow not only for the determination of the optimal use of UPFCs to minimize congestion, but also to estimate their profitability.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the motion of the sprung mass and four degrees of freedom for the unsprung masses. For this model, the equations of motion were derived using Lagrange’s equation. To verify the model, the suspension deformations computed using the model were compared with the deformations measured through three actual vehicle driving tests which are the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating positons of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determine the signal arrival time and then locate the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transfers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

Digital twins are the foundation of autonomous off-road vehicles. Dynamic reduction methods are one of several ways to develop digital twins for off-road vehicles. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results of digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built control, electrical, and control systems.

Context: Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives: We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time. Methods: The key idea is to use on one side natural language processing solutions to detect problems at service level, extracting words that represent anomalies. Clustering and topic modeling techniques have been used to identify patterns and group them with respect to topics. On the other side time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Results: We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. We have observed a correspondence between anomalies in log files and monitoring data, e.g. an increase in memory usage or machine load. The results are extremely promising. Conclusion: Our model requires to integrate site administrators' expertise in order to consider all critical scenario in the data center and understand results properly.

The following article presents an analysis of the determinants of diabetes using a dataset containing the surveys of 2000 patients from the Frankfurt Hospital in Germany. The data were analyzed using the following models, namely: Tobit, Probit, Logit, Multinomial Logit, OLS, WLS with heteroskedasticity. The results show that the presence of diabetes is positively associated with "Pregnancies", "Glucose", "BMI", "Diabetes Pedigree Function", "Age" and negatively associated with "Blood Pressure". A cluster analysis is realized using the fuzzy c-Means algorithm optimized with the Elbow method and three clusters were found. Finally a confrontation among eight different machine learning algorithms is realized to select the best performing algorithm to predict the probability of patients to develop diabetes.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

The Short-Term Assessment of Risk and Treatability (START) is deemed the most appropriate instrument for assessing violence risks and management because of its balanced approach between dynamic risk and protective factors. Although several facets of reliability and predictive validity of this tool were strong, its inter-rater reliability, construct validity, and implementation in Asian population were under-investigated. The objective of this research was to examine the inter-rater reliability and construct validity of the START: Thai version within forensic psychiatric inpatients. The participants consisted of 118 forensic psychiatric inpatients hospitalized at Galya Rajanagarindra Institute in Thailand. Trained mental health professionals (i.e., psychiatrists, forensic nurses, clinical psychologists, social workers, and occupational therapists) assessed each participant across twenty domains of the Thai START. The inter-rater reliability was examined using the intraclass correlation coefficient and a confirmatory factor analysis for ordinal data was used to test the construct validity of the scale. The main finding showed a good-to-excellent inter-rater reliability and supported two relational constructs (i.e., strength vs vulnerability subscales) of the Thai START. The Thai START is a promising tool for using in Thai forensic psychiatric setting but some items were not significant in contributing to the scale. This study also provides the guideline for implementing the tool in non-Western forensic psychiatric populations.

This work presents an approach for detecting mathematical formulas in scanned document images. The proposed approach is end-to-end trainable. Since many OCR engines cannot reliably work with the formulas, it is essential to isolate them to obtain the clean text for information extraction from the document. Our proposed pipeline comprises a hybrid task cascade network with deformable convolutions and a Resnext101 backbone. Both of these modifications help in better detection. We evaluate the proposed approaches on the ICDAR-2017 POD and Marmot datasets and achieve an overall accuracy of 96% for the ICDAR-2017 POD dataset. We achieve an overall reduction of error of 13%. Furthermore, the results on Marmot datasets are improved for the isolated and embedded formulas. We achieved an accuracy of 98.78% for the isolated formula and 90.21% overall accuracy for embedded formulas. Consequently, it results in an error reduction rate of 43% for isolated and 17.9% for embedded formulas.

Rheumatoid arthritis (RA) is an autoimmune disease in which joints are gradually de-stroyed, and early diagnosis and treatment before joint deformation or destruction is im-portant. The detection of novel RA biomarkers in saliva may facilitate the early detection of RA before the onset of disease. In this study, we conducted a comprehensive proteomic analysis of salivary proteins from RA model mice. Proteins were identified by Western blotting and enzyme-linked immunosorbent assay in serum, saliva, and ankle joints from DBA/1JJmsSlc mice, a model of rheumatoid arthritis. Ankle joints and submandibular glands were hematoxylin and eosin stained and immunostained, and the results were compared with those of control mice. Citrullinated α1 antitrypsin (A1AT, 46 kDa) was commonly detected in saliva, serum, and ankle joints of severe RA model mice, and was confirmed by proteome analysis. Western blotting detected a band corresponding to 46 kDa in serum, saliva and ankle joints, and immunostaining of ankle joints with A1AT an-tibody showed a strong positive signal in the synovium. In DBA/1JJmsSlc mice, not only cyclic citrullinated peptide antibodies but also A1AT may be involved in protein citrulli-nation and contribute to the development and severity of RA.

Cancer survival continues to improve in high-income countries, partly explained by advances in screening and treatment. Previous studies have mainly examined the relationship between individual dietary components and cancer prognosis in tumours with good therapeutic response (breast, colon and prostate cancers). The aim of this review was to assess qualitatively (and quantitatively where appropriate) the associations of dietary patterns and cancer prognosis from published prospective cohort studies, as well as the effect of diet interventions by means of randomized controlled trials (RCT). A systematic search was conducted in PubMed, and a total of 35 prospective cohort studies and 14 RCT published between 2011 and 2021 were selected. Better overall diet quality was associated with improved survival among breast and colorectal cancer survivors; adherence to the Mediterranean diet was associated to lower risk of mortality in colorectal and prostate cancer survivors. A meta-analysis using a random-effects model showed that higher versus lower diet quality was associated with a 23% reduction in overall mortality in breast cancer survivors. There was evidence that dietary interventions, generally combined with physical activity, improved overall quality of life, though most studies were in breast cancer survivors. Further cohort and intervention studies in other cancers are needed to make more specific recommendations.

The European Power Exchange has introduced day-ahead auctions and continuous trading spot markets to facilitate the insertion of renewable electricity. These markets are designed to balance excess or lack of power in short time periods, which leads to a large stochastic variability of the electricity prices. Furthermore, the different markets show different stochastic memory in their electricity price time series, which seem to be the cause for the large volatility. In particular, we show the antithetical temporal correlation in the intraday 15 minutes spot markets in comparison to the day-ahead hourly market. We contrast the results from Detrended Fluctuation Analysis (DFA) to a new method based on the Kramers–Moyal equation in scale. For very short term (< 12 hours), all price time series show positive temporal correlations (Hurst exponent H > 0.5) except for the intraday 15 minute market, which shows strong negative correlations (H < 0.5). For longer term periods covering up to two days, all price time series are anti-correlated (H < 0.5).

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

The motivations for the move to electrified vehicles are discussed with reference to their improved energy efficiency, their potential for lower CO2 emissions (if the electricity system is decarbonized), their lower (or zero) NOx/particulate matter (PM) tailpipe emissions, and the lower overall costs for owners. Some of the assumptions made in life-cycle CO2 emissions calculations are discussed and the effect of these assumptions on the CO2 benefits of electric vehicles are made clear. A number of new tribological challenges have emerged, particularly for hybrid vehicles that have both a conventional internal combustion engine and a battery, such as the need to protect against the much greater number of stop-starts that the engine will have during its lifetime. In addition, new lubricants are required for electric vehicle transmissions systems. Although full battery electric vehicles (BEVs) will not require engine oils (as there is no engine) they will require a system to cool the batteries – alternative cooling systems are discussed, and where these are fluid based, the specific fluid requirements are outlined.

Keywords: SARS-CoV-2; COVID-19 ; Non Communicable diseases (NCDs). Clinical features; Institucionalized or hospitalized elderly; meta-analisys.

Muscle mass at admission is important to survive stroke, and stroke-induced sarcopenia is a serious problem because of its poor prognosis. Muscle mass measurement and monitoring are essential for appropriate re-habilitation and nutrition management. Several methods are used to assess skeletal muscle mass in stroke, such as computed tomography (CT), ultrasonography, bioelectrical impedance analysis, dual-energy X-ray absorptiometry, biomarkers, and anthropometrics. In stroke, a head CT is used to estimate muscle mass by measuring the temporal muscle. However, it is mostly retrospectively conducted due to radiation exposure. After stroke, limb muscle atrophy and diaphragm dysfunction are observed using ultrasound. However, ultrasound requires an understanding of the methods and skill. A bioelectrical impedance analysis can be used to assess muscle mass in patients after a stroke unless they have dynamic fluid changes. Dual-energy X-ray absorptiometry is used for follow-up after hospital discharge. Urinary titin N-fragment and serum C-terminal agrin fragment reflect muscle atrophy after stroke. Anthropometrics may be useful with limited resources. We summarized the features of each measurement and proved the recent evidence to properly measure and monitor skeletal muscle mass after stroke.

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.

With a reduction in the mortality rate of burn patients, patient length of stay (LOS) is increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient's expected LOS. However, few have investigated the association between LOS and a patient's mental and socioeconomic status. There is anecdotal evidence for links between these factors and uncovering these will aid in better addressing the specific physical and emotional needs of burn patients, and facilitate the planning of scarce hospital resources. Here, we employ machine learning (clustering) and statistical models (regression) to investigate whether a segmentation by socioeconomic/mental status can improve the performance and interpretability of an upstream predictive model, relative to a unitary model derived for the full adult population of patients. Although we found no significant difference in the performance of the unitary model and segment-specific models, the interpretation of the segment-specific models reveals a reduced impact of burn severity in LOS prediction with increasing adverse socioeconomic and mental status. Furthermore, the models for the socioeconomic segments highlight an increased influence of living circumstances and source of injury on LOS. These findings suggest that, in addition to ensuring that the physical needs of patients are met, management of their mental status is crucial for delivering an effective care plan.

The Doughnut Economy is a new approach for the inclusion of planetary boundaries and social foundation in the development of societies. The Sustainable Development Goals of the UN determine another view for development targets. The developed Sustainability Window approach provides a means for operationalisation and quantification of the Doughnut Economy. The developed method calculates minimum economic development to guarantee sustainable social development and maximum economic development not to exceed environmental sustainability. The developed method, ASA Doughnut, is illustrated with case data from Thailand. The sustainability Doughnut for Thailand has been calculated for both weak and strong sustainability criteria. It seems that strong sustainability is a too strict requirement regarding several environmental dimensions of development while the weak sustainability criteria are fulfilled. The developed method and tool is flexible and can be used for comparative analysis of different countries or regions, for dynamic analysis of sustainability development, for gap analysis of the required improvement of environmental or social efficiency, and analysis of degrowth possibilities. The selection of indicators for the analyses and their reliability is crucial for the validity of the results and usefulness in policy planning.

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

The main purpose of this paper is to propose a novel optimization model with a new machine learning approach in the first section to achieve the best results in financial institutions in the second section. Since the constancy of efficacy derived from parametric and non-parametric is not significant, this paper provides a scientific assessment at the optimization section and proposes a novel combined parametric and non-parametric model which will be a new experiment in literature perception. A scientific assessment of banks based on a combination of the efficiency measurement method of CCR(Charnes, Cooper and Rhodes model) or CRS(Constant Return to Scale) BCC(Banker, Charnes, and Cooper model) or VRS (Variable Return to Scale) in Data Envelopment Analysis (DEA), as well as Stochastic Frontier Approach (SFA) for 65 banks during Feb to July 2020, are introduced. For analyzing the performance of the parametric and non-parametric approaches we have considered the linear regression and Unreplicated Linear Functional Relationship (ULFR). At the machine learning section, a novel four-layers data mining filtering pre-processes for selected supervised classification as well as unsupervised clustering algorithms to increase the accuracy and to remove unrelated attributes and data are applied. For the four kinds of preprocessing approaches of unsupervised attributes, supervised attributes, supervised instances, and unsupervised instances, we have chosen discretization, attribute selection, stratified remove folds, and resample filters respectively. Based on the nature of the suggested financial institution's dataset and attributes, the most appropriate preprocessing filter in each layer to achieve the highest performance is suggested. Finally, the superior bank, best performance model, and the most accurate algorithm are introduced. The results indicate that the bank number 56 is the superior bank. Among the proposed techniques, the novel recommended CVS compared with CCR-BCC and SFA models, has a more positive correlation with profit risk, and show a higher coefficient of determination values. Sequential Minimal Optimization(SMO) algorithm receives the highest accuracy in all four suggested filtering layers.

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

This study identifies new Alkali Activated Composite Mud Mortar to alternate Cement Mortar used for laying masonry works. This study handles both experimental and numerical analysis of strength and durability for mortar, especially bond strength along mortar and brick joints have studied detail and compared with ANSYS. The bond strength has estimated through the triplet brick prism of shear behavior. The strength and deformation characteristics of masonry constituents obtained from these tests depend on the actual composite conduct of masonry. There is always a good reception for environmentally friendly, low energy, new construction materials, and used in preparing mortar/brick unit combinations all around the world. Also, this study fulfills those requirements. Especially, masonry laid with the mud mortar meets all. Fly Ash, Ground Granulated Blast furnace Slag, and Quarry Dust have utilized the new composites' preparation with 8Molority, 10Molority developed excellent strength. The composite specimens are having compression and bond strength, ranging from 4.84-5.14 and 0.12-0.18N/mm2. The mixed ratio of materials has taken (0.5 parts of Fly Ash, 0.5 parts of Steel Slag, 1.75 parts of Soil, and 0.25 parts of Quarry Dust). The samples tested resistance against acidic, sulfate, and alkalinity indicated PH values less than 7 and 9 simultaneously. Compression strength varied from 10-20% and gained more and fit composite used for mortar in masonry works.

Prebiotic oligosaccharides are widely used as human and animal feed additives for their beneficial effects on the gut microbiota. However, there are limited data to assess the direct effect of such functional foods on the transcriptome of intestinal epithelial cells. The purpose of this study is to describe the differential transcriptomes and cellular pathways of colonic cells directly exposed to galacto-oligosaccharides (GOS) and fructo-oligosaccharides (FOS). We have examined the differential gene expression of polarized Caco-2 cells treated with GOS or FOS and their respective mock-treated cells using mRNA sequencing (RNA-seq). A total of 89 significant differentially expressed genes were identified between GOS and mock-treated groups. For FOS treatment, a reduced number of 12 significant genes were observed to be differentially expressed relative to the control group. KEGG and Gene Ontology functional analysis revealed that genes up-regulated in the presence of GOS were involved in digestion and absorption processes, fatty acids and steroids metabolism, potential antimicrobial proteins, energy-dependent and -independent transmembrane trafficking of solutes and amino acids. Using our data, we have established complementary non-prebiotic modes of action for these frequently used dietary fibers.

Surfactants based on polyfluoroalky ethers are commonly used in fire-fighting foams on airport platforms, including for training sessions. Because of their persistence into the environment, their toxicity and their bioaccumulation, abnormal amounts can be found in ground and surface water following operations of airport platforms. As many other anthropogenic organic compounds, some concerns raised about their biodegradation. That is why the OECD 301 F protocol was implemented to appreciate the oxygen consumption during the biodegradation of a commercial fire-fighting foam. However, a Raman spectroscopic monitoring of the process was also attached to this experimental procedure to evaluate to what extent a polyfluoroalkyl ether disappeared from the environmental matrix. The relevance of our approach is to use chemometrics, including the Principal Component Analysis (PCA) and the Partial Least Square (PLS), in order to monitor the kinetics of the biodegradation reaction of one fire-fighting foam, Tridol S3B, containing a polyfluoroalkyl ether. This study provided a better appreciation of the partial biodegradation of some polyfluoroalkyl ethers by coupling Raman spectroscopy and chemometrics. This will ultimately facilitates the design of a future purification and remediation devices for the airport platforms.

Risk-based redispatch optimization is proposed as a methodology to support the Transmission System Operator (TSO) with preventive remedial actions obtained by extending the security constrained unit commitment/economic dispatch with constraints resulting from the risk assessed for the power system. Although being heuristic, the methodology is based on comprehensive dynamic security assessment as time-domain simulations are used, allowing to express the degree of all types of instabilities, e.g., caused by contingencies, in monetary terms. Therefore, the risk is assessed as the expected value of the cost incurred by the TSO. Such approach forms a new pathway to including risk in planning procedures already used by TSOs. Results obtained for the IEEE39 dynamic power system, with costs assigned to load shedding and generator tripping due to single transmission lines short-circuits, are shown as a reference case.

The study focused on two climatic variables i.e. precipitation and stream flow for analysing change in trend of rainfall for the River Ssezibwa Catchment area and stream flow of River Ssezibwa. This Catchment is found in the districts of Mukono and Buikwe in Uganda. In this area agriculture is majorly dependent on rainfall and irrigation on a small scale. However, rainfall occurrence has become unpredictable over the past few years as result of the changes in patterns of weather. This has caused severe effects on the agricultural cropping system as well as caused negative effects on the natural water resources. Stream flow data of 57 years (1960 – 2017) and rainfall data for 35 years (1982 – 2017) on a daily basis was analysed to find out the trend and detect change point. Trend analysis was done by using the non-parametric analysis while the change point detection was carried out by using the Pettit test (1979).Magnitude of trend for the time series data was carried out using Sen’s Slope estimator and Mann – Kendall test was done to determine the trend. Results from the statistical analysis highlighted that; for stream flow the trend was generally positive and change point detected to be in the year 2000 while for rainfall data analysis indicated that the trend was predominantly negative and change point was in the year 1998.

The molecular study of fat cell development in the human body is essential for our understanding of obesity and related diseases. Mesenchymal stem/stromal cells (MSC) are the ideal source to study fat formation as they are the progenitors of adipocytes. In this work, we used human MSCs, received from surgery waste, and differentiate them into fat adipocytes. The combination of several layers of information coming from lipidomics, metabolomics and proteomics enabled comprehensive analysis of the biochemical pathways in adipogenesis. Simultaneous analysis of metabolites, lipids and proteins in cell culture is challenging due to the compound’s chemical difference so that most studies involve separate analysis with unimolecular strategies. In this study, we employed a multimolecular approach using a two–phase extraction to monitor the crosstalk between lipid metabolism and protein-based signaling in a single sample (~105 cells). We developed an innovative analytical workflow including standardization with in-house produced 13C-isotopically labeled compounds, hyphenated high-end mass spectrometry (high-resolution Orbitrap MS) and chromatography (HILIC, RP) for simultaneous untargeted screening and targeted quantification. Metabolite and lipid concentrations ranged over 3-4 orders of magnitude and were detected down to the low fmol (absolute on column) level. Biological validation and data interpretation of the multiomics workflow was performed based on proteomics network reconstruction, metabolic modelling (MetaboAnalyst 4.0) and pathway analysis (OmicsNet). Comparing MSCs and adipocytes, we observed significant regulation of different metabolites and lipids such as triglycerides, gangliosides and carnitine with 113 fully reprogrammed pathways. The observed changes are in accordance with literature findings dealing with adipogenic differentiation of MSC. These results are a proof of principle for the power of multimolecular extraction combined with orthogonal LC-MS assays and network construction. Considering the analytical and biological validation performed in this study, we conclude that the proposed multiomics workflow is ideally suited for comprehensive follow-up studies on adipogenesis and is fit for purpose for different applications.

Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.

Considering citizens' perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps, and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required.

Easter Island (Rapa Nui) deforestation has traditionally been viewed as a single event, synchronous in time and space across the island and caused by Polynesian settlers. However, recent studies have challenged this idea introducing the concept of spatio-temporal heterogeneity and suggesting a role for climate change. This paper presents a continuous paleovegetation record of the last millennium (~960 to ~1710 CE), based on palynological analysis of a peat core from Lake Kao. During this time interval, deforestation was gradual, with three main pulses at ~1070 CE, ~1410 CE and ~1600 CE, likely driven by drought, anthropogenic practices (mostly fire) or the coupling of both. Some forest regeneration trends have been documented after the first and the second deforestation pulses. Forests were totally removed by 1600 CE, coinciding with the full permanent human settlement of the Kao area. Comparison with other continuous palynological records available for the last millennium (Aroi marsh and Lake Raraku), confirms that forest clearing was heterogeneous in time and space, rather than synchronous island-wide.

Although different assessments and evaluations of Dual-Miller cycle performed, specified output power and thermal performance associated with engine determined. Besides, multi objective optimization of thermal efficiency, Ecological Coefficient of performance ( ) and Ecological function ( ) by the mean of NSGA-II technique and thermodynamic analysis performed. The Pareto optimal frontier obtaining the best optimum solution is chosen by fuzzy Bellman-Zadeh, LINMAP, and TOPSIS decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved.

Due to the upward trend in the globalization of sustainability issues and the intense competitive environment, it is evident that higher education institutions need new strategic approaches to succeed. To this end, the inquiry for this paper has been made into the debate about student relationship management. Going through the literature indicates that institutions have mainly perceived the concept as a technological initiative for solving the problems in individual domains, accompanied by uncoordinated efforts. Thus, the aims of this study are to theoretically present critical success factors of this strategic approach and to empirically examine the recognized factors. To do so, confirmatory factor analysis that is a quantitative analytic method was performed. The results and analyses revealed that there has been a significant correlation between the four critical success factors including knowledge management, student relationship management technology, student orientation, and employees’ involvement. It was also found that these factors are significantly correlated with the construct of student relationship management success. The findings have consequently highlighted that in addition to the technological tool, the role of knowledge management, employees’ involvement, and student orientation appeared to be particularly important for the implementation of the application.

5G heterogeneous network overlaid by millimeter-wave (mmWave) access employs mmWave meshed backhauling as a promising cost-efficient backhaul architecture. Due to the nature of mobile traffic distribution in practice which is both time-variant and spatially non-uniform, dynamic construction of mmWave meshed backhaul is prerequisite to support the varying traffic distribution. Focusing on such scenario of outdoor dynamic crowd (ODC), this paper proposes a novel method to control mmWave meshed backhaul for efficient operation of mmWave overlay 5G HetNet through Software-Defined Network (SDN) technology. Our algorithm is featured by two functionalities, i.e., backhauling route multiplexing for overloaded mmWave small cell base stations (SC-BSs) and mmWave SC-BSs’ ON/OFF status switching for underloaded spot. In this paper, the effectiveness of the proposed meshed network is confirmed by both numerical analyses and experimental results. Simulations are conducted over a practical user distribution modeled from measured data in realistic environments. Numerical results show that the proposed algorithm can cope with the locally intensive traffic and reduce energy consumption. Furthermore, a WiGig (Wireless Gigabit Alliance certified) device based testbed is developed for Proof-of-Concept (PoC) and preliminary measurement results confirm the proposed dynamic formation of the meshed network’s efficiency.

A multi-decadal change analysis of the irrigation ponds in Taoyuan, Taiwan was conducted by using multi-source data including digitized ancient maps, declassified single-band CORONA satellite images, and multispectral SPOT images. Supervised LULC classifications were conducted using four textural features derived from the single-band CORONA images and spectral features derived from SPOT images. Post-classification analysis revealed that the number of irrigation ponds in the study area decreased during the post-World War II farmland consolidation period (1945 – 1965) and the subsequent industrialization period (1970 – 2000). However, efforts on restoration of irrigation ponds in recent years have resulted in gradual increases in the number (9%) and total area (12%) of irrigation ponds in the study area.

Polymer flooding is one of the most successful chemical EOR methods, and is primarily implemented to accelerate oil production by sweep improvement. However, other benefits have extended the utility of polymers. During the last decade, it has been evaluated for an increasing number of fields, both offshore and onshore. This is a consequence of improved polymer properties, which extends the use to HTHS (high temperature high salinity) conditions and of improved understanding of flow mechanisms such as for heavy oil mobilization. A key issue in understanding polymer performance is to control and predict in-situ porous medium rheology. The first part of this paper reviews recent developments in understanding polymer flow in porous medium. Especially, polymer in-situ rheology and injectivity is discussed. The second part of this paper reports polymer flow experiments using the most applied polymer for EOR processes, HPAM (partially hydrolyzed polyacrylamide). The experiments address high rate near-wellbore behavior (radial flow) as well as reservoir rate steady state flow (linear flow) and discuss differences observed in terms of flow conditions. In addition, impact of oil on polymer rheology was investigated, and compared to single-phase polymer flow in Bentheimer sandstone rock material. Presence of oil leads to a reduction in apparent viscosity.

Friction and wear take place on two solid surfaces in sliding contact as a result of the mechanical, thermal and chemical interactions with the participation of environmental species. Electron microscopy and the associated spectroscopic analyses are powerful in probing these mechanisms in spatial resolutions from micro to atomic scale. This article provides a review of the author’s work in the wear and friction mechanisms of PVD hard coatings in which various SEM- and TEM-based microscopic and spectroscopic techniques were employed. Understanding on the failure mechanisms and the origin of self-adaptive friction has been improved to nano-scale. Other related issues are also discussed, such as sample preparation techniques for cross-sectional electron microscopy, energy dispersive X-ray spectroscopy and electron energy loss spectroscopy.

The aim of this work was to determine if the development of low-cost or no-cost finite element analysis (FEA) software has advanced to the point where it can be used in place of trusted commercial FEA packages for linear static structural analyses using isotropic material models. Nonlinear structural analysis will be covered in a separate paper. Several suitable packages were identified, these underwent a process of systematic elimination when they were unable to meet the minimum imposed qualitative criteria. Three packages were chosen to be subjected to performance benchmarking, namely: Code_Aster/Salome Meca; Mecway and Z88 Aurora. SimScale, a browser-based analysis package was included as well because it met all the baseline criteria and has the potential to offer a completely cloud-based approach to computer aided engineering, potentially reshaping the way an engineering business views its operational capabilities. This paper presents the test cases and simulation results for packages that fall under the linear static structural analysis type.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over the conventional utility grid systems, however, it faces severe instability issues due to continually increasing constant power loads. To improve the stability of the entire system, load side compensation technique is chosen because of its robustness and cost effectiveness. In this particular occasion, a sliding mode controller is developed for microgrid system in the presence of CPL to assure certain control objective of keeping the output voltage constant at 480V. After that, the robustness analysis of the sliding mode controller against parametric uncertainties is presented. The sliding mode controller robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are illustrated in this paper. Later, the performance of the PID and sliding Mode controller is compared in case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of Sliding Mode controller over PID controller. All the necessary calculations are reckoned mathematically and results are verified in the virtual platform such as MATLAB/Simulink with the appreciable outcome.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

Motivated by diffraction experiments on the $\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$ reconstructed Si(111) due to deposition of rare earth elements (Dy, Tb) and silicide formation we analyse the splitting and non-splitting of superstructure spots. For this purpose, we model diffraction patterns for one dimensional structures generated by the binary surface technique and use supercell models to keep the analysis simple. Diffraction pattern are calculated in the framework of the kinematical diffraction theory and they are analyzed as a function of the domains and domain boundaries. Basic properties of the diffraction pattern are analyzed for model systems of a two-fold and a three-fold periodicity. The rules derived from these calculations are applied to the "real-world" system of Si(111)-($\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$)-RESi$2_{}$ (RE = Dy or Tb). Depending on the combination of domains and domain boundaries of different types a plethora of different features are observed in the diffraction patterns. These are analyzed to determine the sizes of both domain boundaries and domains from experimentally observed splitting of specific superstructure spots.

This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.

Coronavirus Disease-19 (COVID-19) is caused by the infection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The virus enters host cells through receptor-mediated endocytosis of angiotensin-converting enzyme-2 (ACE2), leading to systemic inflammation, also known as a “cytokine storm,” and neuroinflammation. COVID-19’s upstream regulator, interferon-gamma (IFNG), is downregulated upon the infection of SARS-CoV-2, which leads to the downregulation of ACE2. The neuroinflammation signaling pathway (NISP) can lead to neurodegenerative diseases, such as Parkinson's disease (PD), which is characterized by the formation of Lewy Bodies made of α-Synuclein protein encoded by synuclein alpha (SNCA). This study presents the pathways involved in the downregulation of ACE2 following SARS-CoV-2 infection and its effect on PD progression. Through QIAGEN’s Ingenuity Pathway Analysis, the study identifies the NISP as a top-five canonical pathway/signaling pathway and SNCA as a top-five upstream regulator. Core Analysis was also conducted on the associated molecules between COVID-19 and SNCA to construct a network connectivity map. The Molecule Activity Predictor tool was used to simulate the infection of SARS-CoV-2 by downregulating IFNG, which leads to the predicted activation of SNCA, and subsequently PD, through a dataset of intermediary molecules. Downstream Effect analysis was further used to quantify the downregulation of ACE2 on SNCA activation.

This article provides a detailed analysis of non-invasive techniques for prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state-of-the-art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

South Africa’s internationally celebrated water legislation placed the responsibility for investing in water services at the door of the government. In recent years South Africa has struggled to invest adequately to keep up with growing demand for these services. The result is a suite of social, economic and environmental risks that are destabilising to the fiscus and the financial sector. This paper explored both operating and capital costs, and applied the ‘Beyond the Gap’ analysis to show that an additional 55% investment (R91 billion annually) is necessary to ensure South Africa meets its water policy goals. The analysis explores the factors that influence the investment amount and shows that a combination of policy choices and exogenous factors such as climate change could render the investment gap anywhere between 30% and 90% of the estimated current allocation to the water sector. The most significant savings are attributed to reducing the extent of “non-revenue water”, an energy transition away from coal, sharing of some water taps between (up to) five houses, reduced water allocations to agriculture, investments in invasive alien plant (IAP) clearing in water catchments above dams, and water demand management, capacity building and more efficient integration of different bulk water schemes. The study revealed important dependencies between capital and operating costs; where operating costs are ignored, the decay of water infrastructure is accelerated. Showing the contingency of investment needs on policy choices, places responsibility for attaining water policy goals and SDG 6 in the hands of a diverse set of actors. Quantifying the investment need also raises the critical question of how to raise the required investment. Recognising the need to balance risks to financiers with the widespread risks to households, companies and government when investment in water services is inadequate, South Africa is exploring a Monetary Architecture approach of inter-balance sheet relationships in an attempt to mobilise investment from new sources.

The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements. This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios. Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

This paper proposes a methodology for the development and its Verification and Validation(V&amp;V) of safety-rated software. The development methodology is based on the structured analysis and structured design methodology, which has been proven in the field of safety software development during long period. The suggested the development and the V&amp;V methodology additionally focuses on the quality attributes 3C+T (completeness, Correctness, Consistency and traceability), which are important criteria in IEC international standards. The software development life cycle is compiled from IEC international standardization, which are of the requirements analysis phase, design phase, implementation phase, testing phase, installation/commissioning phase, and operation &amp; maintenance phases. The activities of the development process method were defined from requirements analysis to implementation, and the activities of the verification and validation were defined to include the scope of the developer's activities and then to include the test phase, installation/commissioning phase, and operation phases additionally. In other words, testing phase performed by the developer was defined as informal testing, and testing performed by the verifier according to test criteria, test plans, and test procedures was defined as formal testing. According to proposed methodology, the requirements phase defines the sequence of Entity Relationship Diagram (ERD)-Context Diagram-Data Flow Diagram (DFD), functional decomposition process and defines operational scenario. In the design phase, module design such as task analysis and task allocation were defined. In the implementation phase, we listed the coding guidelines that must be followed for safety-related system. The test phase defined the test criteria, test plan, test procedure, and test result report activities. In the installation/commissioning and operation phases, recommendations for improving reliability, quality, and safety were proposed for deliverables such as user documentation and technical documentation.

Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

In this study, the biodegradation properties of leather treated with various finishing chemicals were evaluated in order to enhance the sustainability of leather processing. We applied advanced analytical techniques including FT-IR, thermogravimetric analysis (TGA), and solid-state NMR spectroscopy. Leather samples treated with different polymers, resins, bio-based materials, and traditional finishing agents were subjected to a composting process under controlled conditions to measure their biodegradability. The findings revealed that bio-based polyurethane finishes and acrylic wax exhibited biodegradability, while traditional chemical finishes like isocyanate and nitrocellulose lacquer showed moderate biodegradation levels. The results indicated significant differences in the biodegradation rates and the impact on plant germination and growth. Some materials, such as black pigment, nitrocellulose lacquer and wax, were beneficial for plant growth, while others, such as polyurethane materials, had adverse effects. These results support the use of eco-friendly finishes to reduce the environmental footprint of leather production. Overall, this study underscores the importance of selecting sustainable finishing chemicals to promote eco-friendly leather manufacturing practices.

Bioactive peptides are sequences of amino acids, cut from the parent protein with en-zymes or high pressure processing that can impart a tertiary health benefit to the con-sumer that goes beyond basic human nutrition. This study explores mesopelagic fish in-cluding Maurolicus muelleri and Benthosema glaciale as sources of peptides with an-ti-inflammatory, heart health beneficial, antioxidant and other bioactivities. Bioactive peptides were generated post-catch using different enzymatic hydrolysis processes ap-plied to mesopelagic species caught from experimental cruises targeting these fish. Maurolicus muelleri and Benthosema glaciale were the dominant species processed. Bi-oactive hydrolysates included a Maurolicus muelleri Alcalase hydrolysate and a Maurolicus muelleri autolysis. Hundreds of peptide sequences were identified using mass spectrome-try from active hydrolysates. In silico analysis was used to rank identified peptide se-quences in terms of overall bioactivity using Peptide Ranker, anti-inflammatory activity using PrepAIP, ability to impart Umami flavours with Umami-MRNN and dipeptidyl peptidase IV inhibitory potential using AntiDMPpred . Thirty peptides were selected fol-lowing in silico analysis. Several of the selected peptides had sequence homology with peptides derived from other fish proteins including Atlantic salmon (peptide QCPLHR-PWAL) and thin-lipped mullet (peptide FDAFLPM). Seven peptides predicted to have an-ti-inflammatory, anti-type 2 diabetes or Umami potential using in silico strategies were chemically synthesized and their anti-inflammatory activities confirmed using in vitro bioassays with COX-1 and COX-2 enzymes. The peptide QCPLHRPWAL inhibited COX-1 and COX-2 by 82.90% (+/-0.54) and 53.84% respectively and had a selectivity index greater than 10. This peptide warrant further research as a novel anti-inflammatory/pain relief peptide. Other bioactivities associated with DPP-IV inhibition and Umami flavours de-velopment are discussed. Identified peptides offer potential for use in functional foods or topical agents to prevent pain and inflammation associated with diseases like psoriasis, arthritis and inflammatory bowel disease, however further research in vivo is required.

This study comprehensively explores Serbia's Disaster (Risk) Management System, aiming to critically evaluate its inherent strengths and weaknesses. Using descriptive-analytical methods and qualitative interpretation, SWOT analysis was applied to collect data from scientific papers, books, and institutional reports. This study delves deeply into the nuances of Serbia's Disaster Management Framework, revealing a nuanced landscape of strengths, weaknesses, and strategic improvement opportunities. Identified strengths underscore a robust foundation characterized by proficient professionals, state-of-the-art technology, and cohesive teamwork, all pivotal for effective emergency preparedness and response. Conversely, weaknesses underscore pressing imperatives for enhancements in regulatory enforcement, resource allocation, and infrastructure upgrades, presently hampering the framework's efficacy and response readiness. This research contributes to the ongoing discourse on disaster risk management and serves as a catalyst for further scholarly inquiry and policy formulation. By mapping out the structure of Serbia’s Disaster (Risk) Management Framework and integrating diverse data sources, the study enriches theoretical models and lays the groundwork for practical policy interventions. Its findings, including precise recommendations for policy reform and strategic improvements, provide valuable insights for policymakers, emergency management experts, and stakeholders seeking to enhance disaster preparedness and response capabilities on both national and global scales.

In this paper, a multi-patch weakly singular isogeometric boundary element method (WSIGABEM) for two-dimensional elastostatics is proposed. Since the method based on the weakly singular boundary integral equation, the quadrature techniques, dedicated for the weakly singular and regular integrals, are applied in the method. A new formula for the generation of collocation points is suggested to take the full advantage of the multi-patch technique. The generated collo-cation points are essentially inside the patches without any correction. If the boundary conditions are assumed to be continuous in every patch, no collocation point lies on the discontinuous boundaries, thus simplifying the implementation. The multi-patch WSIGABEM is verified by the simple examples with analytical solutions. The feature of the present multi-patch WSIGABEM is investigated by the comparison with the traditional IGABEM. Furthermore, the combination of the present multi-patch WSIGABEM and the particle swarm optimization algorithm results in a shape optimization method in two-dimensional elastostatics. By changing some specific control points and their weights, the shape optimizations of the fillet corner, the spanner and the arch bridge are verified to be effective.

HIV-associated neurocognitive disorders (HAND) persist under antiretroviral therapy as a complex pathology that has been difficult to study in cellular and animal models. Therefore, we generated an ex vivo human brain slice model of HIV-1 infection from surgically resected adult brain tissue. Brain slice cultures processed for flow cytometry showed >90% viability of dissoci-ated cells within the first three weeks in vitro, with parallel detection of astrocyte, myeloid, and neuronal populations. Neurons within brain slices showed stable dendritic spine density and mature spine morphologies in the first weeks in culture, and they generated detectable activity in multi-electrode arrays. We infected cultured brain slices using patient-matched CD4+ T-cells or monocyte-derived macrophages (MDMs) that were exposed to a GFP-expressing R5-tropic HIV-1 in vitro. Infected slice cultures expressed viral RNA and developed a spreading infection up to 9-days post-infection, which were significantly decreased by antiretrovirals. We also detected infected myeloid cells and astrocytes within slices and observed minimal effect on cellular via-bility over time. Overall, this human-centered model offers a promising resource to study the cel-lular mechanisms contributing to HAND (including antiretroviral toxicity, substance use, and aging), infection of resident brain cells, and new neuroprotective therapeutics.

Background: The present study investigated the outcomes and possible predictive factors of autologous bone marrow cells (BMCs) therapy in patients with „no-option“ critical limb ischaemia (CLI). It was focused on exploring the clinical background and prior statin and renin-angiotensin system (RAS)-acting agent’s pharmacotherapy related to the therapeutic efficacy of BMCs treatment. Methods: In the present study, we have reviewed thirty-three patients (mean age 64.9 ± 10 years; 31 males) with advanced CLI after failed or impossible revascularisation, who were treated with 40 ml of autologous BMCs by local intramuscular application. Patients with limb salvage and wound healing (n=22) were considered responders to BMCs therapy, and patients with limb salvage and complete ischemic wound healing (n=13) were defined as super-responders. Logistic regression models were used to screen and identify the prognostic factors, and a receiver operating characteristics (ROC) curve, a linear regression and a survival curve were drawn to determine the predictive accuracy, the correlation between the candidate predictors and the risk of major amputation. Results: Based on the univariate regression analysis, baseline C-reactive protein (CRP) and transcutaneous oxygen pressure (TcPO2) values were identified as prognostic factors of the responders, while CRP value, ankle-brachial index (ABI) and bone marrow-derived mononuclear cells (BM-MNCs) concentration were identified as prognostic factors of the super-responders. An area under the ROC curve of 0.768 indicated good discrimination for CRP ˃ 8.1 mg/L before transplantation as a predictive factor for negative clinical response. Linear regression analysis revealed a significant dependence between the levels of baseline CRP and the concentration of BM-MNCs in transplanted bone marrow. Patients taking atorvastatin before BMCs treatment (n=22) had significantly improved TcPO2 and reduced pain scale after BMCs transplant, compared to the non-atorvastatin group. Statin treatment was associated with reduced risk for major amputation. However, the difference was not statistically significant. Statin use was also associated with a significantly higher concentration of BM-MNCs in the transplanted bone marrow compared to patients without statin treatment. Patients treated with RAS-acting agents (n=20) had significantly reduced pain scale after BMCs transplant, compared to the NON-RAS group. Similar results, reduced pain scale and improved TcPO2, were achieved in patients treated with atorvastatin and RAS-acting agents (n=17) before BMCs treatment. Results of the Spearman correlation showed a significant positive correlation between CLI regression, responders, and previous therapy before BMCs transplant with RAS-acting agents alone or with atorvastatin. Conclusions: CRP and TcPO2 were prognostic factors of the responders, while CRP value, ABI and BM-MNCs concentration were identified as predictive factors of the super-responders. Atorvastatin treatment was associated with a significantly increased concentration of BM-MNCs in bone marrow concentrate and higher TcPO2 and lower pain scale after BMCs treatment in CLI patients. Similarly, reduced pain scales and improved TcPO2 were achieved in patients treated with atorvastatin and RAS-acting agents before BMCs treatment. Positive correlations between responder and previous treatment before BMCs transplant with RAS-acting agents alone or atorvastatin were significant.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

A regional modern river-sand geochemical mapping study in the majority of the Timor-Leste territory, including the research area, has not been previously reported. This study demonstrated the potential of modern river sand geochemical mapping as an effective method for lithological mapping, particularly in areas that face challenges in conducting efficient geological fieldwork and have limited geological information. A total of 53 modern river sand samples were collected and analyzed. Before examining the samples using wavelength-dispersive X-ray fluorescence for ten major elements, the loss-on-ignition method was employed. In the Clerec drainage basin, SiO2, CaO, and K2O showed positive correlations, and CaO was positively correlated with MnO. Similarly, in the Sahe River catchment, a positive relationship was observed between CaO, MnO, and TiO2. These findings suggested the presence of quartz, manganese, secondary K-bearing minerals, and secondary Ti-bearing minerals in carbonate rocks that have undergone silicification and other alteration processes. The principal component analysis results corroborated the SiO2/Al2O3 and K2O/Na2O ratios and the ICV findings. PC1 was related to the variations in Ca-poor and Ca-rich rocks; PC2 indicated notable inputs from SiO2-rich and SiO2-poor rocks; and PC3 revealed the possibility of contributions from secondary mineral assemblages related to silicification and low-grade metamorphic processes.

Rebar cages form the skeletal framework of reinforced concrete structural members. They comprise longitudinal and transverse reinforcing bars, usually connected by tie wires. Past studies have identified tie-wire connections as the "weak links" in large, prefabricated rebar cages, leading to their failure during site handling and construction stages. Recent research has explored the use of mechanical U-bolt connectors to bolster stability and safety of rebar cages while reducing their vulnerability and risk of failure. This paper presents a comprehensive experimental and numerical investigation of a new type of mechanical connector, referred to as "Cage Clamp", to enhance rebar cage stability and safety. Component-level experimental tests are conducted to determine the force-displacement response of Cage Clamp connectors for crossbar connections in different degrees of freedom. The results highlight the remarkable strength and stiffness of Cage Clamp connectors, significantly outperforming traditional tie-wire connections. Furthermore, full-scale experimental tests are performed on a circular underground pile-shaft rebar cage with Cage Clamp connectors to assess its behavior under various site handling conditions. The data obtained from these experiments are used to construct and refine detailed 3D finite element models of the rebar cages, capable of capturing the complex behavior of rebar cages with Cage Clamp connectors. Using the numerical model, a sensitivity analysis is performed to assess the effect of the number of Cage Clamps on the deflection and stability of the rebar cage. The findings show that using cage clamps to connect pick-up rebar with lateral reinforcement at every 8 to 10 feet is sufficient to ensure the stability of the cage during typical site handling conditions. The results of this study serve as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages, contributing to safe and efficient construction practices.

Coffee Coffea arabica L. depends on abundantly distributed rainfall and drought impacts their development, fruit production, bean quality, and ultimately, beverage quality. Plant Biotechnology by means of genetic manipulation and plant regeneration by the somatic embryogenic process is an alternative technology to overcome these prob-lems. In the present work we used the molecular approach of the trehalose/trehalase role in plant surviving in extreme drought/salt environments. We used a cassette containing the antisense C. arabica L. trehalase gene under the RD29 promoter from A. thaliana and the NOS terminator to genetically modified an em-bryogenic coffee C. arabica L. cv typica line under osmotic stress supplemented with mannitol (0.3M) and sorbitol (0.3M) containing medium. Drought resistant SE lines were recovered and regenerate plants. Resistant SE lines showed a higher rate of competence to induce secondary SE capacity. A transcrip-tome analysis was performed from high competent SE lines to understand the acquired molecular mechanisms to developed SE. From the up-regulated genes, a PPI network made by STRING v12.0 with high confidence (0.700) revealed the presence of the 10 modules. The cell cycle, chromatin remodeling, somatic embryogenesis, oxidative stress, generic transcription pathway, carbon metabolism, phenylpropanoid biosynthesis, trehalose biosynthesis, proline bio-synthesis and glycerolipid metabolism.

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium-sulfur batteries in the second. These results provide insights into the establishment of R&D cooperation strategies by countries and pre-planning by companies in the battery industry.

The traditional manual recording of vaccination records in Germany faced challenges during the COVID-19 pandemic, prompting the introduction of a Covid smartphone app with QR codes. However, this solution brought new challenges, emphasizing the need for a centrally managed European digital vaccination record for efficiency and validity. Thus, this study assesses the feasibility of using the HL7 FHIR standard in the healthcare industry for implementing a digital vaccination pass management and monitoring system. The system aims to offer convenience and improved efficiency for both patients and healthcare providers while promoting interoperability with other healthcare systems. We developed a prototype using modern technologies, such as React, Quarkus, and Keycloak, evaluating the applicability of FHIR in promoting interoperability within the healthcare industry. Results indicate potential benefits for patients and healthcare providers, offering secure access to immunization records, personalized recommendations, and streamlined management. However, challenges in integrating nuanced vaccination processes into the standardized FHIR system, necessitating custom extensions, and issues with manual data entry and identity provider integration, pose obstacles. Despite these challenges, the study suggests that implementing HL7 FHIR can enhance efficiency, data accessibility, and accuracy in the vaccination process, supporting broader digitization efforts in the German healthcare system and beyond.