Advanced Techniques Research Articles

Assessing the influence of environmental variables on energy efficiency changes in the provision of drinking water services

Within the context of the water-energy nexus in drinking water provision, it is crucial to evaluate temporal shifts in energy efficiency. Utilizing the Stochastic Nonparametric Envelopment of Data (StoNED) approach, this research estimates longitudinal variations in the energy efficiency of drinking water services integrating multiple environmental variables. The analysis, conducted in England and Wales from 2008 to 2020, indicates that the source and quality of raw water, as well as population density, influenced the energy performance of water utilities. Quantitative findings evidenced marked disparity in energy efficiency among different companies, with an average efficiency of 0.617 and a range from 0.437 to 0.944. The dynamic assessment indicates an average annual enhancement of 1.1% in the sector, predominantly propelled by technical advancements (0.9%) rather than by improvements in efficiency (0.2%). This study emphasizes the critical need for bespoke policies and incentives to boost energy efficiency and support sustainable urban water management.

The Applicability of The Pecking Order Theory in The Digital Economy

The Peck Order Theory (POT), proposed by Myers and Majluf in 1984, posits that firms prioritize their sources of financing based on the principle of least resistance, starting with internal financing, followed by debt, and ending with equity as a last resort. In the context of the digital economy—which is defined by new finance mechanisms and technical advancements like big data, blockchain, crowdsourcing, and artificial intelligence (AI)—this study investigates the applicability and relevance of POT. This study investigates how these contemporary tools impact conventional financing behaviors and preferences through an extensive literature review.The findings suggest that while POT remains a powerful framework, its application in the digital age needs to be modified to account for the reduced information asymmetry and increased efficiency brought about by digital innovations.

Waste to wealth: Polyhydroxyalkanoates (PHA) production from food waste for a sustainable packaging paradigm

The growing demand for sustainable food packaging and the increasing concerns regarding environmental pollution have driven interest in biodegradable materials. This paper presents an in-depth review of the production of Polyhydroxyalkanoates (PHA), a biodegradable polymer, from food waste. PHA-based bioplastics, particularly when derived from low-cost carbon sources such as volatile fatty acids (VFAs) and waste oils, offer a promising solution for reducing plastic waste and enhancing food packaging sustainability. Through optimization of microbial fermentation processes, PHA production can achieve significant efficiency improvements, with yields reaching up to 87 % PHA content under ideal conditions. This review highlights the technical advancements in using PHA for food packaging, emphasizing its biodegradability, biocompatibility, and potential to serve as a biodegradable alternative to petroleum-based plastics. However, challenges such as high production costs, mechanical limitations, and the need for scalability remain barriers to industrial adoption. The future of PHA in food packaging hinges on overcoming these challenges through further research and innovation in production techniques, material properties, and cost reduction strategies, along with necessary legislative support to promote widespread use.

Consequences of trisomy 21 for brain development in Down syndrome.

The appearance of cognitive deficits and altered brain morphology in newborns with Down syndrome (DS) suggests that these features are driven by disruptions at the earliest stages of brain development. Despite its high prevalence and extensively characterized cognitive phenotypes, relatively little is known about the cellular and molecular mechanisms that drive the changes seen in DS. Recent technical advances, such as single-cell omics and the development of induced pluripotent stem cell (iPSC) models of DS, now enable in-depth analyses of the biochemical and molecular drivers of altered brain development in DS. Here, we review the current state of knowledge on brain development in DS, focusing primarily on data from human post-mortem brain tissue. We explore the biological mechanisms that have been proposed to lead to intellectual disability in DS, assess the extent to which data from studies using iPSC models supports these hypotheses, and identify current gaps in the field.

Green synthesis of ZnO, Fe3O4, and TiO2 nanoparticles: exploring enhanced bacterial inhibition, catalysis, and photocatalysis for sustainable environmental applications

ABSTRACT This study investigates the physicochemical and biological properties of metal oxide nanoparticles (TiO2, ZnO, and Fe3O4) synthesised via the phytosynthesis method using an extract from Boerhaavia diffusa leaves (BDL). The proteins and phenols present in the BDL extract played essential roles in reducing and stabilising TiO2, ZnO, and Fe3O4 nanoparticles. The study reveals size-dependent morphological, structural, and biological characteristics of the produced nanoparticles. FTIR spectra confirm the involvement of BDL extract in the synthesis process of TiO2, ZnO, and Fe3O4 nanoparticles. Transmission electron microscopy (TEM) analysis of TiO2 nanoparticles identified small, spherical particles (9 nm in diameter). BDL-derived TiO2, ZnO, and Fe3O4 demonstrated significant photocatalytic activity, achieving degradation rates of 98.96%, 96.26%, and 94.42%, respectively, in the decomposition of mono-azo dye after 40 minutes of UV irradiation. The reaction rates for BDL-derived TiO2, ZnO, and Fe3O4 were 0.367/min, 0.186/min, and 0.061/min, respectively, when catalysing the reduction of 4-Nitrophenol (4-NP) to 4-Aminophenol using NaBH4. BDL-derived TiO2 nanoparticles exhibited significant antibacterial effectiveness, showing a 23.6 mm zone of inhibition against Salmonella typhi. The BDL-derived TiO2, ZnO, and Fe3O4 nanoparticles exhibited remarkable cytotoxicity against lung cancer cell lines with IC50 values of 76.36 µg/mL, 90.21 µg/mL, and 94.61 µg/mL, respectively. This study reveals the potential of BDL-derived TiO2, ZnO, and Fe3O4 nanoparticles for applications in antibacterial, anticancer, catalytic, and photocatalytic fields, contributing to sustainable development of multifunctional nanomaterials for various technical and environmental advancements.

Ransomware Reloaded: Re-examining Its Trend, Research and Mitigation in the Era of Data Exfiltration

Ransomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or destroying valuable user data and causing numerous disruptions to victims. The severity of the ransomware endemic has generated research interest from both the academia and the industry. However, many studies held stereotypical assumptions about ransomware, used unverified, outdated, and limited self-collected ransomware samples, and did not consider government strategies, industry guidelines, or cyber intelligence. We observed that ransomware no longer exists simply as an executable file or limits to encrypting files (data loss); data exfiltration (data breach) is the new norm, espionage is an emerging theme, and the industry is shifting focus from technical advancements to cyber governance and resilience. We created a ransomware innovation adoption curve, critically evaluated 212 academic studies published during 2020 and 2023, and cross-verified them against various government strategies, industry reports, and cyber intelligence on ransomware. We concluded that many studies were becoming irrelevant to the contemporary ransomware reality and called for the redirection of ransomware research to align with the continuous ransomware evolution in the industry. We proposed to address data exfiltration as priority over data encryption, to consider ransomware in a business-practical manner, and recommended research collaboration with the industry.

To What Extent Can Telehealth/Telemedicine Replace In-Person Doctor Visits In the USA?

The twenty-first century has seen a wave of revolutionary technical advancement that has drastically altered the landscape of many fields. Medicine and healthcare have been affected by these advancements, especially in the areas of artificial intelligence, 3D printing, CRISPR gene editing, and virtual reality. One of the main noticeable advancements however is the rapid rise of telehealth, which has altered the patient-doctor relationship, and is one of the most notable developments in recent decades, notably in the United States. While telehealth doesn’t have a very long history, it rose to popularity during the COVID-19 epidemic, delivering important benefits like as cost-effectiveness, decreased wait times, greater accessibility, and improved mental health assistance. Nonetheless, it can be observed that Telehealth presents some issues, including the possibility of misdiagnosis, difficulties in adapting to technology for older people, and worries about data privacy. Future advancements in telehealth may include selective applications, public education, enhanced security measures, and regulations for artificial intelligence integration. Furthermore, the political atmosphere in the United States may impact and change the rate at which telehealth develops, since it appears to have gained traction in Democratic-leaning areas but not so much in Republican-leaning states. This study will look into the possible benefits, problems, and future advancements of telehealth in the United States, as well as whether it can totally replace in-person doctor visits and to what extent.

Advances in hard X-ray RIXS toward meV resolution in the study of 5d transition metal materials

Resonant inelastic X-ray scattering (RIXS) has played a pivotal role in advancing our understanding of spin-orbit physics in 5d transition metal materials. The progress in RIXS techniques has closely paralleled improvements in energy resolution, which have enabled the study of very low-lying excitations and led to the discovery of numerous new phenomena with significant scientific and technological implications. The multi-bend achromat (MBA) lattice upgrade of third-generation synchrotron sources, such as the Advanced Photon Source (APS), heralds a transformative era by introducing enhancements in brilliance and emittance. These advancements provide an opportunity to push the boundaries of RIXS techniques, meeting the challenges at the research frontiers of material science. This article aims to highlight key instrumental and technical advancements that enable the achievement of meV resolution in RIXS and discuss the impact of such high-resolution RIXS on exploring spin-orbit physics in 5d transition metal materials.

The AI and Big Data Equitable Transformation Theory (ABETT): A Pathway to Inclusive Development and Technological Empowerment

The rapid advancement of artificial intelligence (AI) and big data technology has created new potential for economic growth, social development, and creativity. However, their incorporation into local economies presents issues regarding ethical governance, skill development, access to infrastructure, and equitable benefit sharing. This paper presents the AI and Big Data Equitable Transformation Theory (ABETT), a comprehensive framework for guiding the responsible and inclusive use of AI and big data in various scenarios. The ABETT model has six basic constructs: policy and ethical governance, skills development and education, scalable and equitable infrastructure access, collaborative innovation and knowledge exchange, sustainable AI and data for development, and economic growth and competitiveness. The ABETT framework was created, validated, and enhanced using a qualitative research design that included a systematic literature assessment, expert interviews, and case study analysis. The results support the model's relevance and applicability, illustrating how its elements work together to generate ethical and sustainable technological innovation. Comparisons with previous theories show that ABETT adds value by proposing a comprehensive and customizable pathway for AI and big data adoption that emphasizes collaboration, equity, and sustainability. The paper discusses the model's policy and practical implications, limitations, and future research recommendations. ABETT provides a practical roadmap for stakeholders who want to use AI and big data to promote inclusive growth, sustainable development, and equitable technical advancement.

De Cervantes a Santander: La vía del desarrollo urbano en Manizales

This article seeks to contribute to the construction of urban stories from the introduction of infrastructures and equipment that represented the ideal of progress and modernization during the twentieth century. The case of the city of Manizales is representative, as it presents a model of growth along a structuring road axis, conditioned not only by the geography of the mountain, but by the inclusion of technical advances that favoured economic and social development. The urban expansion aimed at establishing habits related to hygiene, civism and comfort, led to an accelerated urbanization in the east of the city characterized by its high landscape qualities; an architecture framed in typologies of modernization; and the social expectation of achieving a new lifestyle. The socio-economic conditions linked to the passage of the avenue, have kept it as the main artery of the city from its appearance to the present day.

Advances in the Differentiation of hiPSCs into Cerebellar Neuronal Cells.

The cerebellum has historically been primarily associated with the regulation of precise motor functions. However, recent findings suggest that it also plays a pivotal role in the development of advanced cognitive functions, including learning, memory, and emotion regulation. Pathological changes in the cerebellum, whether congenital hereditary or acquired degenerative, can result in a diverse spectrum of disorders, ranging from genetic spinocerebellar ataxias to psychiatric conditions such as autism, and schizophrenia. While studies in animal models have significantly contributed to our understanding of the genetic networks governing cerebellar development, it is important to note that the human cerebellum follows a protracted developmental timeline compared to the neocortex. Consequently, employing animal models to uncover human-specific molecular events in cerebellar development presents significant challenges. The emergence of human induced pluripotent stem cells (hiPSCs) has provided an invaluable tool for creating human-based culture systems, enabling the modeling and analysis of cerebellar physiology and pathology. hiPSCs and their differentiated progenies can be derived from patients with specific disorders or carrying distinct genetic variants. Importantly, they preserve the unique genetic signatures of the individuals from whom they originate, allowing for the elucidation of human-specific molecular and cellular processes involved in cerebellar development and related disorders. This review focuses on the technical advancements in the utilization of hiPSCs for the generation of both 2D cerebellar neuronal cells and 3D cerebellar organoids.

Modified reconstruction approach after pancreaticoduodenectomy optimizes postoperative outcomes: Results from a multivariate cohort analysis.

Despite technical advances in recent decades and a decrease in hospital mortality (<5%), pancreaticoduodenectomy (PD) is still associated with major postoperative complications, even in high-volume centers. The present study aimed to assess the effect of a modified reconstruction technique on postoperative morbidity and mortality. A cohort study of all patients (n=218) undergoing PD between January 2010 and December 2019 was performed at Attikon University Hospital (Athens, Greece). Several variables were studied, including demographic data, past medical history, perioperative parameters, tumor markers and pathology, duration of hospitalization, postoperative complications, 30-day-survival, postoperative mortality and overall survival using multivariate logistic regression and survival analysis techniques. In this cohort, 123 patients [modified PD (mPD) group] underwent a modified reconstruction after a pylorus-preserving pancreaticoduodenectomy, which consisted of gastrojejunostomy and pancreaticojejunostomy on the same loop and an isolated hepaticojejunostomy on another loop. In the standard PD (StPD) group, 95 patients underwent standard reconstruction. The median age was 67 years, ranging from 25 to 89 years. Compared with in the StPD group, the mPD group had significantly lower rates of grade B and C pancreatic fistula (4.9% vs. 28.4%), delayed gastric emptying (7.3% vs. 42.1%), postoperative hemorrhage (3.3% vs. 20%), intensive care unit admission (8.1% vs. 18.9%), overall morbidity (Clavien-Dindo grade III-V: 14.7% vs. 42.0%), perioperative mortality (4.1% vs. 14.7%), and shorter hospitalization stay (11 days vs. 20 days). However, no difference was noted regarding median survival (35 months vs. 30 months). In this single-center series, a modified reconstruction after PD appears to be associated with improved postoperative outcomes. However, further evaluation in larger multi-center trials is required.

Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives

Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives

A review of reinforcement learning for natural language processing and applications in healthcare.

Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings. To systematically explore the applications of RL in NLP, focusing on its effectiveness in acquiring optimal strategies, particularly in healthcare settings, and provide a comprehensive understanding of RL's potential in NLP tasks. Adhering to the PRISMA guidelines, an exhaustive literature review was conducted to identify instances where RL has exhibited success in NLP applications, encompassing dialogue systems, machine translation, question-answering, text summarization, and information extraction. Our methodological approach involves closely examining the technical aspects of RL methodologies employed in these applications, analyzing algorithms, states, rewards, actions, datasets, and encoder-decoder architectures. The review of 93 papers yields insights into RL algorithms, prevalent techniques, emergent trends, and the fusion of RL methods in NLP healthcare applications. It clarifies the strategic approaches employed, datasets utilized, and the dynamic terrain of RL-NLP systems, thereby offering a roadmap for research and development in RL and machine learning techniques in healthcare. The review also addresses ethical concerns to ensure equity, transparency, and accountability in the evolution and application of RL-based NLP technologies, particularly within sensitive domains such as healthcare. The findings underscore the promising role of RL in advancing NLP applications, particularly in healthcare, where its potential to optimize decision-making and enhance patient outcomes is significant. However, the ethical challenges and technical complexities associated with RL demand careful consideration and ongoing research to ensure responsible and effective implementation. By systematically exploring RL's applications in NLP and providing insights into technical analysis, ethical implications, and potential advancements, this review contributes to a deeper understanding of RL's role for language processing.

Impact of CO2 on the Pyrolysis of Mixed Polymer Wastes into Combustible Fuel: A Case Study for Footwear Waste

Plastic waste is a promising resource for producing liquid fuels that can be integrated into existing hydrocarbon infrastructures. However, the heterogeneous nature of plastic-derived liquid fuels limits direct application in internal combustion engines, necessitating their refinement into a usable form. To address these issues, this study explored the enhancement of combustible gaseous fuels derived from plastic waste, footwear waste, as a viable alternative. This approach involves the introduction of carbon dioxide as a reactive feedstock during the pyrolysis process. Analytical techniques were employed to precisely determine the types and compositions of four polymers present in footwear waste. The compositional matrices of the primary pyrogenic products were also identified. However, incorporating carbon dioxide into pyrolysis leads to its interaction with volatile compounds, converting them into lighter gaseous products, particularly carbon monoxide. The homogeneous reactivity of carbon dioxide was further enhanced by the application of heat and a nickel-based catalyst. The gaseous product yield from catalytic pyrolysis in the presence of carbon dioxide increased proportionally with the test temperature. Specifically, the use of carbon dioxide led to a 1.92-fold increase in gaseous product yield at 700 ˚C, in reference to the results from nitrogen. This study demonstrates a technical advancement in pyrolytic valorisation of footwear waste by incorporating carbon dioxide and provides a detailed investigation into its mechanical role in maximising the production of combustible gaseous fuels.

Bibliometric Analysis of Green Technology Research Trends and Their Impact on the Global

Green technology has emerged as a critical field of research due to growing global concerns about climate change and environmental degradation. This study conducts a bibliometric analysis to explore research trends in green technology and their impact on the global economy. The analysis covers publications from 1981 to 2024 and reveals a significant increase in research output, particularly after 2000, driven by global efforts to promote sustainability. Key themes identified include green technology innovation, investment, green growth, and technical advancements in green chemistry and solvents. China and the United States lead in research output and international collaboration, highlighting the global nature of green technology research. However, challenges such as the high costs of green technology adoption and the need for more interdisciplinary research persist. This study concludes that while green technology research has made substantial progress, future efforts must focus on making these technologies more economically viable and accessible, particularly in developing regions.

Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments

Virtual studies involving pedestrians have gained relevance due to the advantage of not exposing them to actual risk, and simulation setups have benefitted from rapid technical advancements, becoming increasingly complex and immersive. However, it remains unclear whether complex setups affecting participants’ freedom of movement impact their decision-making. This research evaluated the effects of a more realistic approach to studying pedestrian crossing behavior by comparing a perception-action task requiring participants to walk effectively along a semi-virtual crosswalk with a similar experiment using static crossing conditions. Using a CAVE system, two real-world streets were modeled in two different virtual scenarios, varying vehicle speed patterns and distance from the crosswalk. Visual stimuli were presented to two groups of 30 participants, with auditory stimuli adapted accordingly. The impact of various factors on participants’ crossing decisions was evaluated by examining the percentage of crossings, crossing start time, and time-to-passage. Overall, the experimental approach did not significantly affect participants’ crossing decisions.

Cardiovascular care with digital twin technology in the era of generative artificial intelligence.

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

The Golgi method. A historical through contemporary view.

Knowledge regarding the biology of the nervous system and its functions has gone through various theoretical, methodological, and interpretative stages throughout history, depending largely on technical advances that have allowed us not only to approach old questions from new perspectives but also to address new ones. One advance that constituted a watershed in the history of neuroscience was the appearance of a chrome-silver staining technique called the Golgi method that allowed the complete, three-dimensional observation of nerve cells. Discovered by Camilo Golgi and, later, modified significantly and employed by Santiago Ramón y Cajal, Golgi's method was crucial in demonstrating the veracity of the Neuronal Theory over the earlier Reticular Theory, and in revealing numerous findings related to the human brain and those of many other animal species, which continue to be analyzed today. Despite a period of scientific recession in the first half of the 20th century, the use of the Golgi method prevailed and even expanded in the second half of that century and into the 21st, as researchers continued to use it in its original or modified form and in combination with emerging methodologies. Currently, there are no signs of any decline in its use.

Evaluating Effects of Continuous Occlusion on Visual Letter Identification

Visual letter identification refers to humans’ use of a visual system to recognize the 26 letters in the English alphabet. Letters may be partially occluded by nonadjacent small occlusion blocks (called discrete occlusion) or a single occlusion block (called continuous occlusion) in the real world, thereby affecting visual letter identification. The effects caused by discrete occlusion have been investigated in existing research. The results indicate that humans possess the ability to identify partially occluded letters and a large occlusion ratio results in low identification accuracy. However, few studies have explored the effects caused by continuous occlusion and revealed precise reflections on occlusion effects. This study presents an evaluation to explore the effects of continuous occlusion on visual letter identification. A controlled experiment is designed to achieve two research goals: identifying the maximum extent of occlusion ratio (named upper bound) that does not affect visual letter identification and investigating the change patterns of the accuracy of occluded letter identification with the gradual increase in occlusion ratio. The experimental results are analyzed in detail, and three main findings are obtained. First, moderately occluded letters can be accurately identified in a continuous occlusion scene. Second, the precise upper bounds are obtained for each of the 26 letters. Lastly, the change patterns for the 26 letters present three nonlinear decline patterns. The findings could inspire technical advancements in the fields of visualization, computer vision, and healthcare.