The study explores the link between corporate social responsibility (CSR) and the financial performance of listed firms in the Philippines. This research used a longitudinal approach to investigate data from the Philippine Stock Exchange Index (PSEi) from 2019 to 2022. The environmental, social, and governance (ESG) framework assessed CSR performance, while financial performance was measured using return on equity and stock return. Firm profiles such as industry type, firm size, firm age, and auditor type were used as control variables. The researchers used firms' sustainability, annual, and governance reports as the primary data sources. The regression analysis results show insufficient data to support the idea that CSR performance affects the firm’s financial performance. It shows that only governance performance is positively correlated and significant with return on equity. Other components and overall CSR performance were found insignificant with return on equity and stock return. This study provides preliminary information on the link of CSR to the financial performance of listed firms in the Philippines. It extends the current literature on CSR performance and its link to financial performance in an emerging economy setting.

Acute kidney injury (AKI) in children is a critical medical condition characterized by a sudden decline in kidney function. This article provides a comprehensive overview of AKI in pediatric populations, exploring its pathophysiology, the role of various drugs, and the long-term implications for kidney health. Key topics include oliguria, anuria, urine output, hypervolemia and interactions among them as well as role of diuretic nephrotoxicity and glomerular filtration rate. Concepts of electrolytes, acid-base balance and renal perfusion assessment are presented. Basic principles of intensive care unit (ICU) management, renal replacement therapy, and the association with multiorgan failure are described. Additionally, the article discusses the potential long-term outcomes of AKI, including the risk of chronic kidney disease, hypertension, and proteinuria.

More than 5% of the global population suffers from disabling hearing loss, primarily sensorineural hearing loss (SNHL). SNHL is often caused by factors such as vascular disorders, viral infections, ototoxic drugs, systemic inflammation, age-related labyrinthine membrane degeneration, and noise-induced hearing loss (NIHL). NIHL, in particular, leads to changes in blood-labyrinth-barrier (BLB) physiology, increased permeability, and various cardiovascular health issues, including hypertension, diabetes, neurological disorders, and adverse reproductive outcomes: Research has connected an elevated risk of hypertension with loud noise. The endocrine and autonomic nervous systems are also linked to arousal following an acute exposure to loud sounds. As a preventive option for both NIHL and hypertension as sequela of noise trauma, we present the transcutaneous vagus nerve stimulation (taVNS). taVNS may be appropriate to prevent NIHL-associated hypertension, as it typically reduces blood pressure and improves heart rate variability. On the other hand, normalizing cerebral blood flow preserves cochlear homeostasis, which is maintained by the BLB.The paper provides a comprehensive overview of NIHL pathophysiology and the impact of noise on auditory structures and function. Our research focuses on the auditory structures and functions affected by noise, such as the strial BLB, as well as the autonomic changes caused by NIHL, specifically the emergence of NIHL-associated hypertension as a primary comorbidity. We describe the clear link between taVNS use and increased HRV and slower progression of chronic hypertension, and we propose it as a cutting-edge preventive option for both conditions.

Cervical cancer (CC) remains a critical global health concern, ranking as the fourth leading cause of cancer-related mortality among women, with an estimated 604,000 new cases and 342,000 deaths in 2020. The primary etiological factor for CC is infection with high-risk strains of the Human papillomavirus (HPV), which often goes unrecognized due to the immune system's ability to clear the virus. Emerging evidence suggests that the vaginal microbiome (VMB) plays a pivotal role in modulating the immune response, influencing oncogenesis, and impacting HPV clearance. This review discusses the dynamics of the VMB, emphasizing its diversity and how various factors, such as hormonal fluctuations and ethnicity, shape its composition. A balanced VMB, characterized by a predominance of Lactobacillus species, is crucial for maintaining local immune defenses and preventing dysbiosis, which can lead to chronic inflammation and increase the risk of CC. Conversely, dysbiotic conditions with reduced Lactobacillus and increased anaerobic bacteria have been correlated with HPV persistence and disease progression. The potential of probiotics to restore a healthy VMB and enhance immune responses offers a promising avenue for CC prevention and management. Further research into the VMB's role in HPV pathogenesis is essential for developing targeted therapeutic strategies to mitigate CC risk.

This study aimed to test the effect of toxic leadership on exit intentions and whether burnout syndrome mediates this relationship. This study's sample consists of 309 participants who work in organizations based in Portugal and Angola. The results indicate that toxic leadership positively and significantly affects burnout syndrome and exit intentions. As for burnout, only the disengagement dimension positively and significantly affects turnover intentions. Disengagement partially mediates the relationship between toxic leadership and exit intentions for participants working in Portugal and a total mediation effect for participants working in Angola. Organizations should be concerned about the leadership style adopted by their leaders, because if there is a toxic leader, this will lead to burnout syndrome in their subordinates and cause them to leave the organization, since turnover intentions are the best predictor of voluntary departure from the organization.

An isolated system always evolves according to unitary evolution and maintain coherence. However the system inevitably interacts with the environment, information about the relative phases between the quantum states leaks into the environment and becomes delocalized, known as environment-induced decoherence. Here we consider the gravitational decoherence of a quantum system near the event horizon of a Schwarzschild black hole. We show that the gravitational decoherence is non-Markovian, which is consistent with information conservation and the Pagecurve-like entanglement dynamics. Moreover, taking the point of view that information on the collapsed matter is stored as the quantum fluctuation of the horizon, the horizon can be regarded as an “uncertain” quantum object instead of random fluctuations of graviton noise. Now the Hawking radiation bath plays the role of the environment, leading to the decoherence of the horizon. Therefore, information about the location of the horizon, or equivalently information about the collapsed matter, is carried away by the Hawking radiation. The memory time of the non-Markovian process and the Page time are estimated.

In 2021, the World Health Organization classified isocitrate dehydrogenase (IDH) mutant gliomas as a distinct subgroup of tumors with genetic changes sufficient to enable a complete diagnosis. Patients with an IDH mutant glioma have improved survival which has been further enhanced by the advent of targeted therapies. IDH enzymes contribute to cellular metabolism, and mutations to specific catalytic residues result in the neomorphic production of D-2-hydroxyglutarate (D-2-HG). The accumulation of D-2-HG results in epigenetic alterations, oncogenesis and impacts the tumor microenvironment via immunological modulations. Here, we summarize the molecular, cellular, and clinical implications of IDH mutations in gliomas as well as current diagnostic techniques.

This paper proposes a multifunctional soft robotic gripper for a Dobot robot to handle sensitive products. The gripper is based on pneumatic network (PneuNet) bending actuators. In this work, two different models of PneuNet actuators have been studied, designed, simulated, experimentally tested, and validated using two different techniques (3D printing and molding) and three different materials: FilaFlex (3D printed), Elastosil M4601 and Dragonskin Fast 10 silicones (with molds). A new soft gripper design for the Dobot robot is presented, and a new design/production approach with molds is proposed to obtain the gripper's PneuNet multifunctional actuators. It also describes a new control approach that is used to control the PneuNet actuators and gripper function, using compressed air generated by a small compressor/air pump, a pressure sensor, a mini valve, etc., and executing on a low-cost controller board – Arduino UNO. This paper presents the main simulation and experimental results of this research work.

Scaling the operation is the single greatest challenge in every enterprise. The more complex the enterprise, the greater the challenge. Scaling in healthcare has been a consistent challenge, with the need to meet clinical endpoints with less staff, increased costs of resources, regulatory oversight, and the goal of improving access to care. The article explores the emerging paradigm of multi-agent AI systems in healthcare, representing a significant leap beyond traditional Large Language Models. We comprehensively analyze the potential of these systems to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. The paper describes a hypothetical sepsis management system comprising seven specialized AI agents, each handling specific aspects of patient care, from data collection and diagnosis to treatment recommendations and resource management. We also examine applications in chronic disease management and hospital patient flow optimization. The technical implementation of these systems is discussed, including the use of advanced Large Language Models, inter-agent quality control measures, implementing guardrails, self-reflection, integration with Electronic Health Records, and the importance of explainable AI for decision transparency. In addition to describing the promising potential benefits, such as enhanced diagnostic accuracy and personalized treatment plans, we address significant challenges, including data quality assurance, workflow integration, and ethical considerations. We conclude the article by highlighting future directions, such as integrating IoT devices and developing more sophisticated natural language interfaces. Our work underscores the transformative potential of multi-agent AI systems in healthcare while emphasizing the need for rigorous validation, ethical oversight, and a patient-centered approach in their development and implementation.

Substance abuse and psychiatric disorders are two prevalent and interrelated public health concerns that often coexist, leading to complex clinical scenarios. This comorbidity poses significant challenges in diagnosis, treatment, and understanding the causal relationships between the two conditions. Frequently, one disorder is neglected in favor of the other, and misconceptions abound regarding which condition precipitates the other. This article explores the common comorbidity of substance abuse and psychiatric disorders, delving into the prevalence, diagnostic challenges, treatment implications, and the bidirectional nature of causality. By examining current research and clinical practice, we aim to shed light on the misconceptions and emphasize the need for integrated approaches to effectively address both conditions concurrently.

This paper presents a method for filtering and extracting noise from ECG signals using a Butterworth filter. The aim is to enhance signal qual- ity, enabling more accurate analysis in biomedical contexts, particularly in arrhythmia detection and other cardiac assessments.

Graph neural networks and graph convolutional networks are effective at representing complex relationships. We attempt to apply them to sports analytics by reviewing existing applications in sports. These methodologies leverage the inherent graph structure of sports data, capturing interactions among players, teams, and game events. By systematically examining various studies, we highlight how GNNs and GCNs can enhance performance prediction, player evaluation, and tactical analysis in sports. Our review emphasizes the unique advantages these models offer over traditional analytical approaches, particularly in their ability to account for the dynamic and interconnected nature of sports activities. We conclude by identifying potential areas for future research and application, suggesting that the integration of advanced graph-based techniques could significantly advance the field of sports analytics.

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis on the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

Generic text embeddings have demonstrated considerable success across a multitude of applications. However, these embeddings are typically derived by modeling the co-occurrence patterns within text-only corpora, which can limit their ability to generalize effectively across diverse contexts. In this study, we investigate methodologies that incorporate visual information into textual representations to overcome these limitations. Through extensive ablation studies, we introduce a novel and straightforward architecture named VisualText Fusion Network (VTFN). This architecture not only surpasses existing multimodal approaches on a range of well-established benchmark datasets but also achieves state-of-the-art performance on image-related textual datasets while utilizing significantly less training data. Our findings underscore the potential of integrating visual modalities to substantially enhance the robustness and applicability of text embeddings, paving the way for more nuanced and contextually rich semantic representations.

You Only Look One (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, choosing selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (e.g., COCO, PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple intersection-over-union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. Thus, YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss.

This study examines the transformative potential of Artificial Intelligence (AI) in international trade, highlighting its significant roles in optimizing trade operations, enhancing trade finance, and improving market access. In trade optimization, through advanced machine learning and predictive analytics, AI helps in demand forecasting, route optimization, and customs processes, ensuring efficient logistics and inventory management. In trade finance, AI automates document processing and risk assessment, democratizing access to finance and enhancing transactional transparency through integration with blockchain technology. In improving market access, AI-driven analytics identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. The study includes case studies of leading companies such as Alibaba, DHL, and Maersk, showcasing how they leverage AI to enhance their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI's practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include data quality, ethical considerations, technological complexity, and public perception. Ensuring high-quality, accessible data, fostering ethical AI usage, and overcoming technological and regulatory barriers are crucial for successful AI integration. Policy recommendations emphasize the need for robust data infrastructure, ethical AI guidelines, and international cooperation to harmonize data protection regulations. These measures aim to create an environment conducive to AI-driven sustainable economic growth and innovation in global trade.

The dynamics of society is enabled, in large part, through an efficient matching between people who have money to lend and people who have a need to borrow. Fair loans serve both the lender and the borrower. This task is handled today mostly via traditional banks. They offer various plans to depositors from whence they serve borrowers. Proposing to replace such rigidity with cyberspace-rooted dynamic ad-hoc global matching of lenders and borrowers. Uniquely here we build a chain of lenders to support a single borrower. Lenders will post a proposition to lend a given amount of money for a stated duration for a set interest rate. Borrowers will post a request to borrow a desired amount of money, for a specified duration, and for which they are ready to pay a stated interest. A virtual digital bank (VDB) will match all the posts, negotiate terms as necessary, and move the money -- everyone benefits. Distributed Digital Lending, DDL, applies to macro, long terms loans, as well as to micro. very short terms loans. DDL efficiency will ensure that all money at rest will be money in growth, and the full global lending capacity will be put to good service through the exact loans borrowers want. DDL is hinged in digital transactions which are immediate, irrevocable, and private, per regulatory allowance. The matching is optimized with AI tools. BitMint*LeVeL is the design standard coin for DDL, but other digital money options will do too.

The advent of the digital age has introduced unprecedented opportunities for communication and connectivity, but it has also given rise to new forms of harassment, collectively termed cyber harassment. This research article provides an in-depth exploration of cyber harassment, examining its prevalence, the profound impact it has on victims, and potential solutions to mitigate its effects.The study is structured around three primary research objectives. Firstly, it investigates the psychological impact of cyber harassment on victims, highlighting the severe emotional and mental health consequences that can arise from such abuse. Secondly, the article scrutinizes the role of social media platforms in both facilitating and combating cyber harassment, analyzing how these platforms can act as both enablers of harassment and as venues for intervention. Thirdly, the research evaluates the current legal frameworks and responses to cyber harassment, considering their effectiveness and suggesting possible improvements. Recent research findings and diverse methodologies are integrated to provide a comprehensive understanding of the multi-faceted nature of cyber harassment. By addressing these key areas, the article aims to contribute to the development of more effective strategies to prevent and respond to cyber harassment, ultimately fostering a safer online environment.

Objective: This study aimed to analyze the general characteristics, job stress, burnout, and turnover intentions of Clinical Research Associates (CRAs). Our objective was to provide a reference point for devising strategies to reduce CRA turnover and burnout rates. Methods: A descriptive, quantitative, and correlative research design was used. A total of 126 CRAs completed a questionnaire that consisted of 115 items, with 21 items on general characteristics, 60 items on occupation-al role, and 10 items on personal strain in job stress. The Burnout Inventory scale was used to investigate 22 items of burnout, and turnover intentions was measured using a 5-item scale. Results: Of the 126 participants, 63 (50%) answered that they intended to leave their job within 1 year & the most of participants were nurse (n = 56, 44.4%). Job stress related to the occupational role of CRA’s vocational stress (r = 0.518), burnout (r = 0.406), and turnover intention (r = 0.181) of personal strain were correlated with each other. Job stress due to personal strain had an explanatory power of 18.7% as a factor influencing turnover intention. Conclusions: To reduce the turnover intention of CRAs, who play an important role in clinical research, it is necessary to find ways to re-duce job stress and burnout.

Geothermal energy generation faces challenges in efficiency, partly due to restrictions on reinjection temperatures caused by scaling issues. Therefore, developing strategies to prevent scaling is critical. This study aims to simulate the scaling tendencies and corrosion effects of geothermal fluids on various construction materials used in scaling reactor/retention tank systems. A range of materials, including carbon steel, austenitic stainless steel, duplex stainless steel, two proprietary two-part epoxy coatings, and thermally sprayed aluminium (TSA), were tested in a simulated geothermal brine. Experiments were conducted in a laboratory vessel designed to replicate the wall shear stress conditions expected in a scaling reactor. The tests revealed varying scaling tendencies among the materials, with minimal corrosion observed. The dominant scale formed was calcium carbonate, consistent with geochemical modelling. The findings suggest that despite the high operating temperatures, the risk of corrosion remains low due to the brine's low chloride content, while the wettability of materials after immersion may serve as a useful indicator for selecting those that promote scaling.

This paper introduces the novel concept of Time-Traveling Turing Machines (TTTMs) — Turing Machines with the ability to read symbols that have been explicitly sent from their future computational state. We delve into their properties and behavior, particularly emphasizing the importance of maintaining a self-consistent computational timeline. We present theorems demonstrating the universality of these machines and the challenges in achieving faster computations than traditional Turing Machines.

This study presents the development and implementation of a remote monitoring system for a dual-axis solar tracker, integrating Industry 4.0 technologies such as IoT, SCADA, and MQTT. The system, which combines a Siemens S7-1200 PLC, an IoT gateway, and a SCADA platform based on Ignition, demonstrated significant increases in solar capture efficiency, with production gains of 25-30% compared to fixed systems, reaching up to 67.65% under ideal conditions. In addition to improving operational management through predictive monitoring and early fault detection, the study identified areas for improvement, such as the need for real-world testing and the integration of weather predictions. In conclusion, the research validates the feasibility and effectiveness of automated IoT and SCADA-based systems to optimize solar trackers, significantly contributing to improved energy efficiency in photovoltaic installations.

Peptidergic GPCR systems are broadly distributed in the human body and regulate numerous physiological processes by activating complex networks of intracellular biochemical events responsible for cell regulation and survival. Their failure due to overaction, ill-function, or blockade gives rise to cell disturbances, eventually causing disease if compensatory mechanisms do not suffice. Revision of updated experimental research provided an evident relationship associating peptidergic GPCR malfunction with tumor formation and maintenance resulting from uncontrolled cell proliferation and migration, colonization, inhibition of apoptosis or altered metabolism, and increased angiogenesis in tumoral tissues. Determination of the implication of GPCR peptide signaling in specific neoplasia is crucial to designing tailored pharmacological treatments to counteract or dismantle the origin of the signaling circuitry causing cellular disruption. In some cases, particular ligands for these receptors may serve as concomitant treatments to aid other pharmacological or physical approaches to eradicate neoplasias.

Optical Coherence Tomography (OCT) is a non-invasive diagnostic technique that renders an in vivo cross-sectional view of the retina. OCT utilizes a concept known as interferometry to create a cross-sectional map of the retina that is accurate to within at least 10-15 microns. Due to the transparency of the eye, OCT has gained wide popularity as an ophthalmic diagnostic tool. The OCT pictures of the retina are classified into “AMD, CNV, DRUSEN, DMR, DR, MH, CSR, and Normal Eye” using a lightweight deep neural network. There are two types of FD-OCT – swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) – both of which acquire spectral interferograms which are then Fourier transformed to obtain an axial scan of reflectance amplitude versus depth. Normal OCT features show a clear and defined retinal structure. The random forest algorithm is a robust ML algorithm that can be used to classify OCT images into four classes: choroidal neovascularization (CNV), deep margin elevation (DME), drusen, and normal. OCT (Optical coherent tomography) is an imaging method used to generate a picture of the back of the eyes and retinas. By using an interferometer, part of the light is directed to the sample and another portion is sent to a reference arm with a well-known length. Interferometers merge two or more sources of light to create interference patterns. We split the light into two beams that travel different optical paths and are combined to produce interference. Optical coherent tomography (OCT) generates sub-surface images of translucent or opaque materials at a resolution equivalent to a low power microscope. OCT build up clear 3D images of thick samples by rejecting background signals whilst collecting light directly reflected from a surface of interest. OCT systems use broadband sources and split light in a fiber coupler, first on a direct reference mirror, and as the sample is being measured. Balanced OCT systems can include additional attenuators at the detector arm to improve the detection efficiency.

Harvesting is the collection of mature crops from the field. A harvesting procedure involves cutting, installation, collecting, transferring, and then stacking the cut produce. Mechanization of harvesting operations is critical to lowering harvesting costs, crop production costs, crop loss, turnaround time, weather risk, and increasing benefit through proper technology. To achieve the desired purpose, a manually operated reaper machine is created and developed to assist farmers by lowering the cost of crop cutting and collection in the field while achieving the best efficiency possible within the limits. Research technique is a set of acts or procedures that must be completed in order to do research properly, as well as the intended sequence of these steps. Identify the problem statement. Studying the current design of the reaper machine Review the literature. Product development general design process steps for reaper harvester’s machine include planning, concept development, system-level design, detail design, testing and refining, and product ramp-up. In this step, the material properties are defined based on design. To simulate the reaper machine using Catia software, Prototyping, testing for validation, Interpret and report. Material is chosen based on the strength requirements of the different parts of the Reaper machine. The machine is built with locally accessible materials. This self-propelled reaper moves at a forward speed of 2.54 m/s. The cutting bar measures 120 cm in length and the bundling mechanism is dropped. The field capacity is 0.306 ha/hour, respectively. The needed manpower for harvesting one hectare of wheat and rice reaper harvesting field operation is two man-hours per ha. The amount of fuel required to completely fill the tank after harvesting the plot was measured to determine the amount of fuel consumed for reaping the test plot, which was 1.17 l/h.

Flavonoids are secondary plant metabolites involved in biological activity that are present in plentiful proportions in many plant origin foods and beverages. Owing to their chemical difference and qualities that make them beneficial for human health, they have received a lot of focus in research. This review therefore presents an account on the chemical structure of flavonoids, food sources and their metabolism with emphasis on bioavailability. And they also examine their antioxidant, anti-inflammatory, anticancer, cardiovascular protective, neuroprotective, and antimicrobial properties and reveal related molecular processes. In addition, it overviews the clinical data concerning their possible use in the prevention and treatment of chronic diseases. Some of the problems that attend current flavonoid research are also dealt with; such issues as variation in the bioactivity of flavonoids and scarcity of clinical studies. This review is designed to present current state of knowledge and to provoke further research concerning the further potential of flavonoids in enhancing human health.

This research explores how regional socioeconomic variables affect the perception of social trust and support networks (PYCC) in Italian regions, and examines the implications for public policy designed to strengthen social cohesion. This study examines the variable "People You Can Count On" (PYCC) from the ISTAT-BES dataset, focusing on its distribution across Italian regions between 2013 and 2022. Using clustering through a k-Means algorithm optimized with the Silhouette coefficient and the Elbow method, three distinct clusters of regions emerged, highlighting significant differences in social support networks. An econometric model was employed to estimate the PYCC variable, factoring in socioeconomic indicators such as employment rates, income inequality, and social participation. The results indicate a complex interplay between socioeconomic conditions and social trust, with regions in the South and Islands showing increased community support, while many Northern regions experienced declines. The study suggests that areas with lower economic conditions often foster stronger social networks, driven by necessity. These findings underline the importance of targeted public policies aimed at fostering social cohesion, particularly in regions facing economic challenges. Policy implications include enhancing education, supporting small enterprises, and promoting social housing and welfare initiatives. Strengthening community participation and volunteering are also highlighted as critical strategies to build resilient support networks. Overall, the research provides valuable insights into the regional disparities of social trust and the role of socioeconomic factors in shaping community support across Italy.

The chronic high fat dysbiosis diet (HFD) instigates cardiac remodeling, dysfunction and chronic heart failure (CHF). The therapeutic approaches focusing oxidative stress, inflammation and hyperlipidemia have not yielded significant positive clinical outcomes. Recent studies report that although mitochondrial ATP citrate lyase (ACLY) supports cardiac function, however, decreases more in human HFpEF than HFrEF. Interestingly, the ACLY synthesizes lipids and creates hyperlipidemia. Epigenetically, ACLY acetylates histone. The mechanism(s) are unknown. One hypothesis is that the HFD induces epigenetic folate 1-carbon metabolism (FOCM), heperhomocysteinemia (HHcy, a cause of abrogation of sleep-time dipping in blood pressure and hypertension) and heart failure. Interestingly, the probiotics utilizes the fat/lipids post-biotically, and increases the mitochondrial bioenergetics and attenuates the HFD induced heart failure. We suggest a novel and paradigm-shift epigenetic mitochondrial sulfur transsulfuration pathways that selectively target HFD-induced CHF. The HFD via transport and metabolomic pathways instigates cardiac remodeling and dyfunction. Previous studies from our laboratory, using a single cell analysis, revealed increase in transporter (SLC25A) of s-adenosine-methionine (SAM) during elevated levels of homocysteine (Hcy, i.e., hyperhomocysteinemia, HHcy), a consequence of impaired epigenetic recycling of Hcy back to methionine by increase in FOCM methylation of H3K4, K9, H4K20 and gene writer (DNMT) and decrease in eraser (TET/FTO). The Hcy transported to mitochondria by SLC7A for clearance via sulfur metabolomic transsulfuration by 3-mercaptopyruvate sulfur transferase (3MST). Interestingly, the HHcy decreases ATP, OCR, H₂S, PGC1α, TFAM, and increase in fission/fusion ratio, causing mitochondria dysfunction. We suggest that gut dysbiosis by HFD disrupts rhythmic epigenetic memory via FOCM and increase in DNMT1 and creates HHcy, leading to decrease mitochondrial transsulfuration and bioenergetics. The treatment with a probiotic, lactobacillus will eat the fat/lipids post-biotically and bi-directionally will produce folic acid and lactone-ketone body that will mitigate the HFD-induced mitochondrial remodeling and heart failure.

Atherosclerosis is a leading cause of cardiovascular disease, characterized by lipid accumulation and chronic inflammation within arterial walls. Early detection in young adults is crucial for preventing adverse cardiovascular events. This study investigates the associations between inflammatory indices, lipid biomarkers, and the presence of atherosclerosis in patients aged 18 to 55 years. Methods: A cross-sectional study was conducted involving 89 participants divided into two groups: 62 patients with documented atherosclerosis (main group) and 27 healthy controls without significant atherosclerosis. Comprehensive data—including demographic information, medication use, imaging results, laboratory parameters, and calculated inflammatory indices (SIRI, SII, AISI, NLR, PLR, MLR)—were collected. Statistical analyses included correlation assessments, group comparisons using the Mann-Whitney U test, logistic regression modeling, feature importance analysis with Random Forest and Gradient Boosting classifiers, receiver operating characteristic (ROC) curves, and K-means clustering. Results: Significant differences were observed between the main and control groups. Patients with atherosclerosis exhibited elevated inflammatory indices (SIRI, NLR, MLR, SII) and lipid profile abnormalities (higher TC and LDL-C, lower HDL-C). Lp(a) and ANGPTL3 levels were significantly higher in the main group (p < 0.001 and p < 0.01, respectively). Logistic regression identified SIRI and ANGPTL3 as significant predictors of atherosclerosis, with the model demonstrating high accuracy (77%) and sensitivity (93%). Feature importance analysis confirmed the significance of SIRI and ANGPTL3, alongside traditional lipid biomarkers, in predicting disease presence. ROC analysis showed excellent model performance (AUC > 0.80). Clustering analysis revealed two distinct patient subgroups characterized by predominant inflammatory profiles or lipid metabolism disturbances. Conclusions: Systemic inflammation and lipid abnormalities play significant roles in early atherosclerosis among young adults. Elevated SIRI and ANGPTL3 levels are potent predictors of disease presence. The integration of inflammatory indices and lipid biomarkers into predictive models enhances risk stratification and supports personalized medicine approaches. Further research with larger cohorts is warranted to validate these findings and inform targeted prevention and intervention strategies.

The availability of petroleum asphalt, derived from non-renewable sources, is steadily declining alongside diminishing petroleum reserves. To reduce reliance on petroleum, alternative renewable natural sources are being explored as both modifiers and replacements for petroleum asphalt, particularly in asphalt binders. This study explores the chemical, rheological, and mechanical properties of Gopal, a bio-asphalt derived from Gondorukem (gum rosin) and crude palm oil (CPO). Two Gopal variants, Gopal-GEM130 and Gopal-GEG90, were analyzed using FTIR and EDX methods, with Pen 60 petroleum asphalt as a control. The results reveal that Gopal-GEG90 and Gopal-GEM130 exhibit 86% chemical similarity to Pen 60 asphalt. Gopal-GEM130 is less toxic and less alkaline compared to Pen 60, while Gopal-GEG90 is less toxic but more alkaline. Both variants fall under the same classification as Pen 60 in terms of rheological properties, with Gopal-GEG90 demonstrating slightly better stripping resistance but lower aging resistance. Gopal-GEM130 showed better stripping resistance and rutting performance, though with lower fatigue resistance and faster aging compared to Pen 60 asphalt. Both bio-asphalt variants align with performance grade PG64S, signifying their potential as alternatives to conventional petroleum asphalt.

Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness and tardiness seen in such a secure environment. The parallel development described by this protocol ensures that any sensitive information collected by police, and missing to academics, remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry - a suspected vehicle attack - can be embedded into such a police library of criminal plots.

Gut dysbiosis and an inflamed bowel are growing concerns in mammals, including dogs. Probiotic supplements have been used to restore the natural microbial community and improve gastrointestinal health. Biofilm formation, antimicrobial activities, and immunological responses of probiotics are crucial to improving gut health. Thus, we tested a commercial probiotic blend (LabMAX-3), a canine kibble additive comprising Lactobacillus acidophilus, Lacticasibacillus casei, and Enterococcus faecium for their ability to inactivate common enteric pathogens; their ability to form biofilms, epithelial cell adhesion and anti-inflammatory response on Madin-Darby Canine Kidney (MDCK) cell line. Probiotic LabMAX-3 blend, or individual isolates, showed a strong inhibitory effect against Salmonella enterica, Listeria monocytogenes, enterotoxigenic Escherichia coli, and Campylobacter jejuni. LabMAX-3 formed biofilms comparable to Staphylococcus aureus. LabMAX-3 adhesion to the MDCK cell line (with or without lipopolysaccharide, LPS) pretreatment showed comparable adhesion and biofilm formation (p < 0.05) to L. casei ATCC 334 used as a control. LabMAX-3 had no cytotoxic effects on the MDCK cell line during 1 h exposure. The IL-10 and TNFα ratio of LabMAX-3, compared to the L. casei control, showed a significant increase (p < 0.05), indicating a more pronounced anti-inflammatory response. The data show that LabMAX-3, a canine kibble supplement, can improve gastrointestinal health.

Lignin is nature's second most abundant vascular plant biopolymer, playing significant roles in mechanical support, water transport, and stress responses. This study identified 90 lignin biosynthesis genes in the rice based on phylogeny and motif constitution, and they belong to PAL, C4H, 4CL, HCT, C3H, CCoAOMT, CCR, F5H, COMT, and CAD families. Duplication events contributed largely to the expansion of these gene families, such as PAL, CCoAOMT, CCR, and CAD families mainly attributed to tandem and segmental duplication, respectively. Microarray data of 33 tissue samples covering the entire life cycle of rice suggested, fairly high PAL, HCT, C3H, CCoAOMT, CCR, COMT, and CAD gene expression, and rather variable C4H, 4CL, and F5H expression. Some members of lignin-related genes (OsCCRL11, OsHCT1/2/5, OsCCoAOMT1/3/5, OsCOMT, OsC3H, OsCAD2, and OsPAL1/6) were expressed in all tissues examined. The expression patterns of lignin-related genes can be divided into two major groups with eight subgroups, each showing a distinct co-expression in tissues representing typically primary and secondary cell wall constitutions. Some lignin-related genes were strongly co-expressed in tissues typical of secondary cell walls. The combined HPLC analysis showed increased lignin monomers (H, G, and S) in five genotypes from young to old growth stages. Based on 90 genes' microarray data, 27 genes were selected for qRT-PCR gene expression analysis. Four genes (OsPAL9, OsCAD8C, OsCCR8, and OsCOMTL4) were significantly negatively correlated with lignin monomers. Furthermore, eleven genes were co-expressed in certain genotypes during secondary growth stages. Among them, six genes (OsC3H, OsCAD2, OsCCR2, OsCOMT, OsPAL2, and OsPAL8) were overlapped with microarray gene expressions, highlighting their importance in lignin biosynthesis.

The spread of false information can significantly harm public opinion, underscoring the importance of accurately identifying untrustworthy news. This paper presents an innovative machine learning (ML) tool, TMining, designed to evaluate news credibility and facilitate various text-mining tasks. By examining a range of ML methodologies alongside preprocessing techniques, we aim to boost the system's effectiveness. Our research meticulously assesses different datasets, highlights the impact of applying stemming techniques, and employs Local Interpretable Model-Agnostic Explanations (LIMEs) to shed light on the rationale behind model predictions. The outcomes reveal a notable enhancement in both the precision and clarity of the news verification process. The ultimate version of the model has been made available as an Application Program Interface (API), and its source code has been shared openly, encouraging further exploration and collaboration within the scientific community. This initiative advances our ability to discern manipulative context from fictitious content and promotes transparency and understanding in the domain of ML applications.

Late-stage epithelial ovarian cancer (EOC) involves widespread dissemination of malignant disease throughout the peritoneal cavity, often accompanied by ascites. EOC metastasis relies on the formation of multicellular aggregates, called spheroids. Given that Liver Kinase B1 (LKB1) is required for EOC spheroid viability and LKB1 loss in EOC cells decreases tumor burden in mice, we investigated whether the LKB1 complex controls invasive properties of human EOC spheroids. LKB1 signalling was antagonized through CRISPR/Cas9 genetic knockout of LKB1 and/or RNAi-dependent targeting of STE20-related kinase adaptor protein (STRAD; an LKB1 activator). EOC spheroids expressing nuclear GFP (green) or mKate2 (red) constructs were embedded in Matrigel for real-time live-cell invasion monitoring. Migration and invasion were also assessed in spheroid culture using Transwell chambers, spheroid reattachment, and mesothelial clearance assays. The loss of LKB1 and STRAD signalling decreased cell invasion through Matrigel and Transwell membranes, as well as mesothelial cell clearance. In the absence of LKB1, zymographic assays identified a loss of matrix metalloproteinase (MMP) activity, whereas spheroid reattachment assays found that coating plates with fibronectin restored the invasive potential. A three-dimensional EOC organoid model demonstrated that organoid area was greatly reduced by LKB1 loss. Overall, our data indicated that LKB1 and STRAD facilitated EOC metastasis by promoting MMP activity and fibronectin expression. Given that LKB1 and STRAD are crucial for EOC metastasis, targeting LKB1 and/or STRAD could disrupt the dissemination of EOC, making inhibitors of the LKB1 pathway an alternative therapeutic strategy for EOC patients.

The last wave of Artificial Intelligence is dedicating particular emphasis to the line of explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided-diagnosis systems and its usage for knowledge discovery are collecting interest in the Medical Imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI were focused on interpreting the predictions returned by deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This raises the attention in proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process in learning and identifying representative prototypical parts from the input images, and they are collecting increasing interest and results in MI applications. This narrative review provides a summary of existing PP networks, their application in MI analysis and current challenges.

This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI's potential to revolutionize traditional practices.

This study investigates the impact of Artificial Intelligence (AI) on traditional firms, specifically examining how scalable network properties and the integration of Environmental, Social, and Governance (ESG) considerations affect financial margins, sustainability, and bankability. The research addresses the core question of how AI investments can drive economic value by enhancing EBITDA and financial stability while aligning firms with sustainability goals. Using advanced network theory and multilayer models, the study reveals that AI-driven scalability significantly improves a firm's financial performance, making it more attractive to investors and financial institutions. Additionally, the integration of ESG factors further enhances operational efficiency, market valuation, and long-term financial stability. The study introduces an innovative "with-or-without" analysis to quantify the comparative benefits of AI and ESG integration, underscoring the role of these technologies in driving sustainable growth and addressing bankability concerns in traditional firms. This comprehensive approach offers new insights into the evolving dynamics of AI, ESG, and corporate finance.

Sleep disturbances and chronic pain are prevalent and interrelated conditions that significantly impact individuals' quality of life. Understanding the intricate dynamics between sleep and pain is crucial for developing effective treatments that enhance the well-being of affected individuals and reduce the economic burden of these debilitating conditions. This narrative review examines the complex relationship between sleep disturbances and chronic pain. We describe the prevalence and types of sleep disturbances and sleep disorders in chronic pain patients. Posteriorly we critically review the clinical and experimental evidence investigating the relationship between sleep disturbances and chronic pain aiming to clarify the impact of chronic pain on sleep and, conversely, the impact of sleep disturbances on pain perception. We conclude that a bidirectional relationship between chronic pain and sleep disturbances is almost a consensus in the literature, however, the strength of each direction of the association is less clear. As of now, the literature suggests that sleep impairment is a stronger predictor of pain than pain is a predictor of sleep impairment. Additionally, addressing sleep disturbances in chronic pain patients is crucial, as poor sleep has been linked to higher levels of disability, depression, and pain-related catastrophizing.

Recent epidemiologic studies have revealed an upward trend in young-onset colorectal cancer (YOCRC) overall, whereas specific data on young-onset colorectal neuroendocrine neoplasms (YONEN) remain limited. This study investigates demographic characteristics and survival trends in YONEN and compares these with those of young-onset colorectal adenocarcinoma (YOADC), the most common histologic subtype of YOCRC. A retrospective analysis was conducted from 2000 to 2019 using the Surveillance, Epidemiology, and End Results (SEER) database. Survival outcomes were assessed using univariate and multivariable Cox proportional models, with demographic differences evaluated via Wilcoxon Rank Sum and Chi-square tests. Out of 61,705 patients aged 20-49 with colorectal cancer, 8% had NEN, and 92% had adenocarcinoma. The YONEN cohort had a higher proportion of Black patients and a lower proportion of White patients than the YOADC cohort (21% vs. 13% and 44% vs. 57%, respectively). NEN was more commonly found in the rectum (79%), and adenocarcinoma was mostly colonic (57%) in origin. YONEN patients had better survival than YOADC patients. Multivariate analysis in YONEN patients revealed that Hispanic patients had better overall survival compared to White patients (HR 0.67, 95% CI 0.47-0.95, p=0.024). Racial disparities should be investigated further to aid in policymaking and targeted interventions.

Introduction: Patients with hematological malignancies undergo intensive treatment and prolonged hospitalization, thus having a variety of physical and psychosocial symptoms and worse quality of life (QOL). Aim: This study aimed to assess the QOL and investigate the symptoms of hospitalized hematological cancer patients. Methods: A cross-sectional study was conducted in the hematology clinics and day units of two general hospitals of Heraklion, Crete. Adult patients with hematological malignancy and an adequate understanding of the Greek language participated. A demographic questionnaire, the European Organization for Research and Treatment for Cancer quality assessment questionnaire (EORTC QLQ-C30), and the MD Anderson Symptom Inventory (MDASI) were used for data collection. The level of statistical significance was set at p &lt;0.05. Results: The sample consisted of 120 patients, 42.5% of whom were women, with a mean age of 65.6 years. The mean time from diagnosis was 33 months. The global health status of QoL had an average value of 47.1. The highest levels of QOL were found in the subscale of cognitive function (72.8) and the lowest in the role function (46.1). Among the of EORTC QLQ-C30 symptoms scale, the lowest score was found in nausea-vomiting (11.0) and the highest in fatigue (59.1). In the MDASI, in part I (core symptoms), higher levels, but also medium intensities were reported at fatigue (78.3%, mean 3.5), drowsiness (65.0, mean 3.3), and distress (65.8%, mean 2.8). In part II, enjoyment of life (85.8%, mean 5.1) had the highest and relation with other people (67.5%, mean 3.7) the lowest scores. The increase in the severity of the core symptoms (part I) was related to females (rho = 0.193, p &lt;0.05) and comorbidities (rho = 0.220, p &lt;0.05). It also related to a significant decrease in all functional domains and an increase in fatigue (rho = 0.571, p &lt;0.05) of the EORTC QLQ-C30 questionnaire. The increased global health status was related to males (rho = -0.185, p &lt;0.05) and physical functioning with younger age (rho = -0.331, p &lt;0.05), higher education (rho = 0.239, p &lt;0.05), fewer months from diagnosis (rho = -0.199, p &lt;0.05) and low comorbidity (rho = -0.209, p &lt;0.05). Finally, the increased global health status was significantly related to lower symptoms determined by the total average symptom score (-0.491, p &lt;0.05). Conclusions: The QoL of hematological cancer patients is significantly decreased during treatments due to a significant number of symptoms that must be taken into consideration for high-quality individualized care.

Minimizing optical losses in resonant cavities is crucial for improving photonic device performance. This study focuses on the development of a simulation tool to analyze scattering losses in Fabry-Pérot interferometers (FPI), offering precise modeling of waveguide dynamics and contributing to accurate loss predictions across various platforms. Optical cavities often suffer from scattering losses due to surface roughness and material defects. Our approach integrates theoretical models and simulations to quantify these losses, utilizing the FPI as a model system. We identified upper and lower reflectivity thresholds, beyond which accurate measurement of losses becomes unreliable. For reflectivity below a certain threshold, measurement errors arise, while excessively high reflectivity can reduce fringe visibility and introduce detector sensitivity issues. Simulations were used to validate the model's ability to predict reflectivity and attenuation in waveguides with varying loss levels. The software’s flexibility to adjust transmission parameters for different cavity configurations enhances its utility for a broad range of photonic systems. Our study offers a novel methodology for optical loss analysis, with practical applications in optimizing photonic devices. By providing a reliable tool for precise loss measurement, this work supports advancements in optical technologies, enabling the design of more efficient, high-performance devices across various applications.

This thesis presents a comprehensive review of cancer, with a particular focus on its physical and biochemical basis. Cancer, a leading cause of mortality worldwide, is characterized by the uncontrolled growth and spread of abnormal cells. Understanding the fundamental differences between cancerous and normal cells is essential for developing effective diagnostic tools and therapeutic strategies. The review begins by exploring the physical anomalies of cancer cells, including their irregular shape, size, and structural organization. These cells often exhibit altered cell membranes, disrupted cytoskeletal architecture, and irregularities in cell adhesion and communication, which contribute to their invasive and metastatic potential. The biochemical basis of cancer is examined in detail, with an emphasis on the dysregulation of key metabolic pathways. Cancer cells undergo profound metabolic reprogramming, often referred to as the Warburg effect, where they preferentially utilize glycolysis over oxidative phosphorylation, even in the presence of oxygen. This metabolic shift supports the rapid proliferation of cancer cells and their survival in hypoxic conditions. Additionally, cancer cells produce abnormal proteins that contribute to tumor progression, such as mutant p53, and fail to produce critical tumor suppressor proteins like RB1, leading to unchecked cell division and evasion of apoptosis. Furthermore, the thesis reviews the role of oncogenes and tumor suppressor genes in cancer development, highlighting how mutations and epigenetic alterations drive the transformation of normal cells into malignant ones. The complex interplay between these genetic changes and the tumor microenvironment is also discussed, emphasizing the importance of signaling pathways, immune evasion, and angiogenesis in cancer progression. This detailed review synthesizes current research and insights from authoritative sources, including peer-reviewed journals and foundational medical texts, to provide a thorough understanding of the physical and biochemical underpinnings of cancer. The findings underscore the importance of continued research in these areas to advance the diagnosis, treatment, and prevention of cancer.

This paper addresses the historically biased selection of problems in technoscience, which has been predominantly shaped by Western male perspectives. We propose a novel framework with objective criteria for identifying and prioritizing human problems worthy of scientific and technological attention. Our approach aims to transcend traditional biases, incorporating a more inclusive, global perspective. We outline metrics considering factors such as scale, urgency, potential impact, and relevance across diverse populations. Using these criteria, we identify key global challenges including climate change, pandemic preparedness, food and water security, education access, sustainable energy transition, AI governance, biodiversity conservation, and mental health. We present a network analysis of their interconnections, revealing complex interdependencies and potential cascading effects of interventions. Our mathematical framework, based on graph theory and network analysis, provides a rigorous foundation for understanding and quantifying these relationships. This research contributes to decolonizing and degendering technoscience, offering a more equitable framework for addressing pressing human problems \cite{roy2018}. Our findings have significant implications for research prioritization, policy-making, and resource allocation in scientific and technological endeavors, potentially reshaping how we approach and address the most critical issues facing humanity.

Mycotoxin contamination of food and feed is a worldwide problem that needed to be addressed with highly efficient and biologically safe techniques. RNA interference (RNAi) is a natural mechanism playing an important role in different processes in eukaryotes, including regulation of gene expression, maintenance of genome stability, protection against viruses and others. Recently, RNAi-based techniques have been widely applied for the purposes of food safety and management of plant diseases, including those caused by mycotoxin-producing fungi. In this review, we summarize the current state-of-art of RNAi-based approaches for reducing aggressiveness of key toxigenic fungal pathogens and mycotoxin contamination of grain and its products. The ways of improving RNAi efficiency for plant protection and future perspectives of this technique, including progress in methods of double-stranded RNA production and its delivery to the target cells are also discussed.

Agriculture is the largest sector of Nepalese economy among the south-Asian developing countries that contributed 26.98 % share of Gross Domestic Product (GDP) in the year 2018/19. The capacity to derive maximum output by per unit of resource is resource use efficiency. Resource use efficiency of a producer explains the comparison between inputs and output value. Potato is staple vegetable in every parts of Nepal. Cobb-Douglas production function is used to predict the significance of inputs in income from cash crops. Due to lack of optimum utilization of available resources in cultivation of vegetable like lack of technical knowledge, low education level, and extensive service the productivity and production is low in Nepalese economy. It ensures vegetable security in Nepal. Though, there are extensive researches on the resource use efficiency and productivity issues on agriculture, very few papers explored in the case of Sudurpaschim province of Nepal OBJECTIVE: This study had the specific purposes: (1) To identify the factors affecting in resource use efficiency and productivity of potato production (2) to utilize the resource use efficiency and productivity in potato production and (3) to show the type of returns to scale in potato production. METHODS: Applying post-positivist paradigm and quantitative causal survey research design, it explored the resource use efficiency and productivity through the semi-structured questionnaire from 140 households by random sampling. Data analysis was carried out by multiple regression model using Cobb-Douglas production function. SPSS software was used to find quantitative results. From the analysis of data using mean, and SD, t-test, analysis of variance, Durbin-Waston test, Marginal Value Product and marginal Factor cost, findings of this research was emerged. RESULTS AND DISCUSSION: The major findings: education level, extension service, farm size and pesticide cost were significant variables in potato production; seed, hired labor, Family Yard Manure and pesticide cost were overused and needed to decrease their cost for optimum allocation and there was decreasing returns to scale in production. This study emphasized on increasing education level, extensive service and farm size for optimum utilization of resources and increasing returns to scale. SIGNIFICANCE: Barriers to Indian potatoes and increasing returns to scale are only possible through protection policy of government. For optimum use of resources and increasing returns to scale, government can introduce capital intensive technique. This study can be implied in policy making and improving existing situation of agriculture, and allocating budget.

The topic of predictive toxicology has been greatly influenced by recent progress in comprehending drug toxicity processes and enhancing medication development. The integration of omics technologies, such as transcriptomics, proteomics, and metabolomics, with traditional toxicological assessments has yielded extensive knowledge about the biological pathways implicated in drug-induced toxicity. The utilization of a multi-omics method amplifies the ability to identify biomarkers that can detect toxicity at an early stage, hence enhancing the safety profile of novel therapeutic medicines. Machine learning and in silico models, such as QSAR models and multi-task deep learning algorithms, have become essential tools. They have shown great accuracy in predicting toxicity endpoints and have helped in the identification of new biomarkers and therapeutic targets. The introduction of microphysiological systems and PBPK modeling has enhanced the transfer of preclinical discoveries to clinical results, providing more precise forecasts of human reactions to medications. Notwithstanding these progressions, obstacles such as the diversity of data and the complex nature of omics data require sophisticated computational techniques for efficient analysis. Continued cooperation and established procedures are crucial to fully utilize these technologies, guaranteeing the creation of safer and more efficient medicinal agents.

Effective leadership is reflected by evidence-based attributes, competencies, and behaviors. The Future of Nursing Report (2011) identified a gap in leadership knowledge, recommending development of creative and innovative strategies to strengthen nursing leadership curricula. These results provided an impetus and charge for nursing education to reexamine and revise leadership content. This mixed methods study analyzed baccalaureate and masters nursing students’ development of their leadership knowledge and skills. Through reflections, undergraduate (n=60) and master’s (n=52) students identified their perceived priorities and tips for success for nursing leaders across the healthcare continuum. Qualitative content analysis aligned students’ comments with the American Organization for Nursing Leadership (AONL) Nurse Executive Competencies (2015). Descriptive data provided the summaries of which leadership competencies were represented. Alignment to the competencies varied, commensurate with the level of educational program: BSN-level management skills vs. MS-level executive skills. Common responses across both programs prioritized communication, professionalism, and leadership behaviors. The results provide an opportunity for students’ synthesis of leadership skills and practices, may guide educators in the design of multi-level leadership curricula, and support development of clinical leaders in practice. This study was prospectively registered with the Campus Institutional Review Board on January 23, 2023, protocol code: 0086-001.

The water exit problem, which is ubiquitous in ocean engineering, is one of the significant research topics in the interaction between navigators and water. The study of the water exit problem can help to improve the structure design for marine ships and underwater weapons for better strength and movement status. However, the water exit problem involves complex processes such as gas-liquid-solid three-phase coupling, cavitation, water separation, liquid surface deformation and fragmentation, making it challenging to study. According to the works carried out by many researchers towards this issue, we summarize the recent developments from three aspects, i.e. theoretical research, numerical simulation and experimental results. In theoretical research, the improved von Kaman model and linearized water exit model are introduced. Several classical experimental devices, data acquisition means and cavitation are introduced in experimental development. Three methods, namely, BEM(boundary element method), FVM(finite volume method) and LES(large eddy simulation) are concluded for numerical simulation. The limitations and shortcomings of these three aspects are then analyzed. Finally, an outlook on the future research improvements and developments of the water exit problem is provided.

The investigation of exoplanetary habitability is integral to advancing our knowledge of extraterrestrial life potential and detailing the environmental conditions of distant worlds. In this analysis, we explore the properties of exoplanets situated with respect to circumstellar habitable zones by implementing a sophisticated filtering methodology on data from the NASA Exoplanet Archive. This research encompasses a thorough examination of 5,595 confirmed exoplanets listed in the Archive as of March 10th, 2024, systematically evaluated according to their calculated surface temperatures and stellar classifications of their host stars, taking into account the biases implicit in the methodologies used for their discovery. Our findings elucidate distinctive patterns in exoplanetary attributes, which are significantly shaped by the spectral classifications and mass of the host stars. The insights garnered from our study not only enhance the existing models for managing burgeoning exoplanetary datasets, but also lay foundational groundwork for future explorations into the dynamic relationships between exoplanets and their stellar environments.

The neural system has limited resources to interact with the environment, and this is where attention comes into play. Attention is a complex process that provides numerous benefits, but there has been a longstanding debate about the mechanisms involved in attentional orientation. Two widely proposed mechanisms are top-down and bottom-up, which relate to the organism's current behavioral goal and the salience of environmental stimuli, respectively. However, distinguishing between these mechanisms can be challenging in both behavioral and neural studies. This review introduces a new conceptualization in which both top-down and bottom-up mechanisms are viewed as aspects of the same process. This unified approach aims to prepare the organism to face stimuli and events based on its prior experiences, immediate (the current task that he/she is aware of), and innate (to be alive) demands.

Triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors, human epidermal growth factor receptor 2, and progesterone receptors (PR). TNBC has the poorest prognosis among breast cancer subtypes and is more likely to respond to immunotherapy due to its higher expression of PD-L1 and a greater percentage of tumor-infiltrating lymphocytes. Immunotherapy has revolutionized TNBC treatment, especially with the FDA's approval of pembrolizumab (Keytruda) combined with chemotherapy for advanced cases, opening new avenues for treating this deadly disease. Although, immunotherapy can significantly improve patient outcomes in a subset of patients, achieving the desired response rate for all remains an unmet clinical goal. Strategies that improve responses to immune checkpoint blockade, including combining immunotherapy with chemotherapy, molecularly targeted therapy, or radiotherapy may improve response rates and clinical outcomes.  In this review, we provide a short background on TNBC and immunotherapy and explore the different types of immunotherapy strategies that are currently being evaluated in TNBC. Additionally, we review why combination strategies may be beneficial, provide an overview of the combination strategies, and discuss the novel immunotherapeutic opportunities that may be approved in the near future for TNBC.

Paper presents proposition of possible extension of The Standard Model Classification of Particles and Fields in way to include gravitation into it.

Pakistan is an agricultural country, and an exporter of crops in many countries. The crop production is an important factor to earn revenue for the country. But the agriculture of Pakistan is still not revolutionized and no modern system has been deployed commonly. Whereas the farmers face difficulty in growing some crops because those crops needs more care and calculated steps. Moreover the farmers face difficulty at different stages of the crop growth like to check fertility of soil and if the soil is non-fertile then which chemical property is lacking. The objective of this research is to provide digitize the agriculture of Pakistan. An emerging technology IoT and Deep learning based system will be developed. The IoT part contains some devices like sensors,gateways and communication technologies whereas the software part consists of Deep learning models that can make predictions about the fertility of the soil. The motive of this system is to provide best results in term of accuracy. Whereas the dataset used is publicly available on kaggle but it was of non-fertile values, so data augmentation is also performed to generate data for fertile values. We have implemented LSTM,RNN,CNN among them LSTM performed best for the prediction of soil fertility.

One of the most crucial attributes of emulsions is their particle size, which has implication on the stability, rheology as well as sensory properties in different food applications. Homogenization speed is an important parameter that disrupts fat globules into desirable particle sizes. The effect of emulsification composition and homogenization parameters on particle size distribution of palm stearin oil-in-water emulsion was investigated. A palm stearin oil-in-water emulsion was homogenized at different speed (10k-25k rpm) and the particle size distribution was investigated. When the homogenization speed was increased above >10k rpm, the particle size distribution decreased. Increasing concentration of surfactant from 1 wt.% to 3wt.%, in the emulsion showed a decrease in particle size distribution for all homogenisation speed. Oil droplet size could also be minimized by increasing the concentration of sugar in continuous phase.

Tegucigalpa, the capital city of Honduras, has the highest number of landslides recorded in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. The present study focused on assessing the physical vulnerability of houses and the risk linked to landslides in these types of soils. As an input for this assessment, the study generated landslide risk vulnerability functions based on empirical data. The study's findings suggest that the estimated Annual Average Losses associated with landslides in this region is USD 7.26 million. Moreover, the study developed a novel approach for the spatial and temporal estimation of landslide hazard probability, which overcomes some of the limitations identified in previous landslide risk assessment studies.

White adipose tissue (WAT) and gut are involved as inducers of neuroinflammation when they detect injury and trigger inflammation. The autonomous nervous system innervates both tissues, although the roles of the sympathetic, parasympathetic and enteric nervous systems have not been fully elucidated. Here, we revisited the participation of both types of inflamed peripheral tissue in neuroinflammation. We first analyzed how inflamed peripheral WAT plays a key role in neuroinflammation once metainflammation is installed. Second, we described the impact of insulin resistance (IR) on hypothalamic dysfunction, T2DM and neurodegenerative diseases. Finally, we analyzed the gut-brain axis, examining cell interactions, soluble factors, the sensing of microbes and the role of the intestinal microbiota and mucosal barriers in neuroinflammation. Since intestinal mucosa and secondary lymphoid organs are densely innervated by the sympathetic, parasympathetic, and enteric nervous systems, we analyzed bidirectional crosstalk between different tissues, neurons, and immune systems in healthy and pathological circumstances to restore normal tissues functions, and consequently homeostasis.

Background: Acute ischemic stroke treatment has been thoroughly studied to identify strategies to reduce treatment times. However, many centers still do not meet the benchmark time metrics. Additionally, smaller centers often face longer treatment times, yet studies focus primarily on larger, more advanced centers. Objectives: The aim of this study is to analyze existing literature to understand the strategies implemented in primary and comprehensive stroke centers to reduce their treatment times, and categorize the studies based on methods used to improve their processes Results: Three main categories of improvements were identified in the literature: 1) standardization of processes, 2) resource management, and 3) data collection. Both primary and comprehensive stroke centers were able to reduce treatment times through standardization of processes. However, challenges such as variations in resources between hospitals and difficulties in integrating data collection software into workflow were highlighted. Additionally, many strategies to optimize resource management and data collection were conducted in comprehensive stroke centers, which can benefit primary stroke centers. Conclusions: Many existing strategies to improve treatment times are viable for both primary and comprehensive stroke centers. However, while data collection is recognized as crucial for process improvement, challenges persist in integrating data collection methods into clinical workflow. Proposed solutions include the development of easy-to-use software tailored to clinician needs.

In this study, it was analyzed that the characteristics of ammonia and their effects on secondary particulate matter (PM) formation by region in Korea during 2020. The NH3 concentration was high in GJ (11.4 ppb) neighboring agricultural area followed by DJ (9.0 ppb) and SE (8.6 ppb) locating in the urban area. On the other hand, BI (2.6 ppb) and JI (4.5 ppb), which are background regions, were lower than other area. Seasonally, ammonia was high in spring and summer, and it generally increased when human activities are active. Therefore, it is believed that ammonia in the atmosphere changes depending on not only local emissions but also temperature-dependent phase distribution characteristics. For SE and GJ, regions with relatively high ammonia concentrations, the effect of ammonia on secondary PM formation was conducted. In both regions, the ammonium-to-sulfate mole ratio tended to increase with an ammonia or PM2.5 concentration increasing. It can be assumed that PM2.5 concentration increases as nitrates are formed under the ammonia sufficient condition. The adjusted gas ratio is generally greater than 4, indicating that there is a lot of free ammonia. Thus, it is estimated that reduction of ammonia would not effective to restrain the nitrate formation.

Biochar has a carbon-based porous structure that is advantageous for adsorbing air pollutants. The pore structure of biochar ranges from several tens to hundreds of m²/g depending on the production temperature. We compared the pore characteristics of biochar with its NOx and SOx adsorption performances to evaluate its adsorbing potential. Biochar was produced from cypress biomass via slow pyrolysis in a fixed-bed reactor and CO2 partial gasification. Slow pyrolysis experiments at 500–700°C were conducted to produce biochar, followed by yield, proximate, elemental, and surface area analyses. CO2 partial gasification activated the biochar produced by slow pyrolysis at the same temperature, and similar analyses were performed. NO and SO2 adsorption experiments were conducted separately and together for biochar produced under each condition. Biochar produced at 600°C showed the highest specific surface area. CO2 partial gasification increased the surface area at each temperature, reaching 680.3 m²/g at 700°C. In single adsorption experiments, N2 carbonized biochar showed NO and SO2 adsorption efficiencies of 84–90% and 88.2–91.4%, respectively, with similar trends in simultaneous adsorption experiments. CO2 activated biochar exhibited higher adsorption efficiencies due to its increased surface area, with NO and SO2 adsorption at 89.8–93.9% and 92–98.9%, respectively. Biochar activated at 700°C simultaneously adsorbed approximately 99% NO and SO2. The NO adsorption efficiency increased with increasing surface area of the small pores, whereas the SO2 adsorption efficiency improved with the development of both small and large pores.

The following article analyses the relationship between Non-Performing Loans and innovation systems at a global level. The analysed data were obtained from the World Bank and the Global Innovation Index for the period 2013-2022 for 149 countries. The analysis was conducted using clustering with the k-Means algorithm, optimized with the Silhouette coefficient and the Elbow method. Subsequently, econometric analysis was carried out using panel data with Random Ef-fects and Fixed Effects. The clustering results show that the optimal number of clusters is three. The econometric analysis highlights the positive relationship between Non-Performing Loans, the exports of cultural and creative services, and the Innovation Input Sub-Index. The results also show the negative relationship between Non-Performing Loans, the H-Index, and the exports and imports of ICT services.

This paper focuses on the concepts used in Quantum Cryptography and recent progress made. We look at the different categories of key distribution protocols that have been proposed and issues related to those. We then take an overview of two main approaches of communication using quantum cryptography concepts, i.e., ``Quantum Secure Direct Communication'' and ``Deterministic Secure Quantum Communication'', and limitations related to the usage of these schemes in a real-world system. At last, we list down the problems identified in the research and conclude our discussion by proposing areas of focus for future research.

Therizinosaurs, a unique clade of theropod dinosaurs, are characterized by their distinct morphological features and enigmatic ecological role. This review provides a comprehensive analysis of the functional morphology and ecological significance of these remarkable dinosaurs. Therizinosaurs are distinguished by their elongated, often curved claws, robust postcranial skeletons, and herbivorous diet, which starkly contrasts with their carnivorous theropod relatives. We explore the evolutionary implications of their morphology, focusing on adaptations such as their specialized feeding apparatus and limb structure. These features suggest a complex interplay between their herbivorous lifestyle and ecological niche. The review synthesizes recent findings from fossil evidence, including detailed studies of cranial and postcranial anatomy, to elucidate the functional mechanisms behind their unique adaptations. Additionally, we discuss the broader ecological role of therizinosaurs within their Mesozoic ecosystems, highlighting their interactions with contemporaneous flora and fauna. By integrating paleontological data with comparative anatomy and ecological modeling, this review aims to shed light on the evolutionary trajectory of therizinosaurs and their impact on our understanding of theropod diversity and adaptation. This focused review contributes to a more nuanced appreciation of therizinosaurs' place in dinosaur evolution and their adaptive strategies in prehistoric environments.

The advent of artificial intelligence (AI) has revolutionized digital marketing, offering unprecedented levels of personalization. AI-driven personalization leverages machine learning algorithms and data analytics to tailor content, advertisements, and recommendations to individual consumers based on their behavior, preferences, and demographics. This paper explores the effectiveness of AI-driven personalization in enhancing customer engagement, increasing conversion rates, and improving overall marketing efficiency. It also examines the ethical considerations associated with these technologies, including data privacy, consent, and the potential for manipulation or bias. While AI-driven personalization can lead to more relevant and engaging consumer experiences, it raises critical ethical questions that must be addressed to ensure responsible and fair use. The study concludes with recommendations for marketers and policymakers to balance the benefits of AI personalization with the need for ethical standards and consumer protection.

Perovskite-silicon tandem solar cells represent a significant advancement in photovoltaic technology, aiming to overcome the efficiency limitations of traditional silicon-based solar cells. This paper explores recent developments in the fabrication and optimization of these tandem cells, focusing on improving both efficiency and long-term stability. The combination of perovskite materials with silicon allows for broader spectrum absorption, which enhances the overall power conversion efficiency. Key strategies include optimizing the perovskite layer's composition, improving the interface between layers to reduce recombination losses, and incorporating advanced encapsulation techniques to mitigate degradation from environmental factors such as moisture and UV exposure. The review also highlights the challenges faced in scaling up production while maintaining high performance and stability. Emerging trends in material engineering and device architecture are discussed, providing insights into future directions for making perovskite-silicon tandem solar cells a viable commercial technology. This study underscores the potential of these hybrid systems to significantly boost the efficiency of photovoltaic systems, offering a promising pathway towards more sustainable and costeffective solar energy solutions.

In this study, we obtained results for the computation of eigen-pairs, singular value decomposition, pseudo-inverse, and the least square problem for elliptic quaternion matrices. Moreover, we established algorithms based on these results and provided illustrative numerical experiments to substantiate the accuracy of our conclusions. In the experiments, it was observed that the p-value in the algebra of elliptic quaternions and elliptic numbers directly affects the performance of the problem under consideration. Selecting the optimal p-value for problem-solving and the elliptic behavior of many physical systems make this number system advantageous in applied sciences.

In previous investigations, drones have been used for visual inspections of heritage elements. We now propose the use of aerial thermography to complement the information obtained with standard drones, which perform purely visual inspections. Thus, after a brief overview of the contribution of aerial thermography to heritage, we will use a drone with a thermographic camera to inspect three unique and important heritage elements: the Roman Bridge of Alcántara, the Roman Aqueduct of Segovia and the Roman Bridge of Alconétar. The three monuments were previously visually inspected with a drone with a camera. These operations will enable us to draw conclusions about the applicability of this technology to heritage: the inspections will demonstrate the usefulness of thermography for the diagnosis of damage to stone at a deeper level, which is already in an advanced stage of development in the interior of the element when it appears on the surface. Detecting such damage early can be the key when trying to adopt less aggressive and expensive early treatment, thus avoiding further risk to the constituent elements of the monument.

With the emergence of the Internet and WWW has become a comprehensive source of information for the extraction of meaningful information has become a significant challenge in the past decade. Furthermore, the available information is classified in unstructured, structured, and semi-structured forms. Numerous amount of information on the web is also characterized in an unstructured or semi-structured format. This usually extracts the potential useful information from these multiple forms and has been considered a leading area of research in this modern era. The authors presented web content mining as a subcategory of web mining that focuses on important extracting patterns from the contexts available in the documents on the web. Hence, this paper focuses on the multiple content mining concepts, tools, and techniques implemented on the documents available on WWW.

Alzheimer's Disease (AD) and Parkinson's Disease (PD) are two neurodegenerative diseases posing a significant disease burden due to their increasing prevalence and socio-economic cost. Traditional therapeutic approaches for these diseases exist but provide limited symptomatic relief without addressing the underlying pathologies. This review examines the potential of immunotherapy, specifically monoclonal antibodies (mAbs), as disease-modifying treatments for AD and PD. We analyze the pathological mechanisms of AD and PD, focusing on the roles of amyloid-beta (Aβ), tau (τ), and alpha-synuclein (α-syn) proteins. We discuss the latest advancements in mAbs therapies targeting these proteins, evaluating their efficacy in clinical trials and preclinical studies. We also explore the challenges faced in translating these therapies from bench to bedside, including issues related to safety, specificity, and clinical trial design. Additionally, we highlight future directions for research, emphasizing the need for combination therapies, improved biomarkers, and personalized treatment strategies. This review aims to provide insights into the current state and future potential of antibody-based immunotherapy in modifying the course of AD and PD, ultimately improving patient outcomes and quality of life.

The startup ecosystem in Peru has experienced remarkable growth in recent years, driven by technol- ogy and innovation across various industries. This article presents a web application developed in PHP, designed to centralize and facilitate access to information about startups, incubators, and investors in Peru. The methodology employed encompasses several stages, from establishing prerequisites to data preprocessing, model selection and training, rigorous evaluation, and the technical development of the platform. The application offers key functionalities such as exploring a comprehensive database of star- tups, detailed access to information about incubators and accelerators, identification of investors, and advanced analysis tools for market proposal prediction. Additionally, it integrates educational resources and allows users to suggest new startups through detailed surveys. The results demonstrate that the ap- plication improves access to relevant data, fosters collaboration among ecosystem actors, and supports informed decision-making. The platform also stands out for its security and privacy measures, as well as its ability to drive innovation and competitiveness in the market. In conclusion, the application has a sig- nificant impact on the Peruvian entrepreneurial ecosystem, and its continuous improvement is essential for the sustainable growth of startups in Peru and globally.

Triazole derivatives of fluorescein containing N,N-dimethylaminopropyl fragments and their ammonium salts were synthesized with yields of 74 - 85%. The resulting compounds exhibit fluorescent properties in the green region of the visible spectrum. The critical aggregation concentration (CAC) was estimated using a py-rene fluorescent probe corresponding to a range of 0.28 - 1.43 mM, and at concentrations above the CAC the compounds form stable aggregates ranging from 165 to 202 nm. A relative quantum yield of 5-24% has been calculated based on fluorescence and UV spectra. The best value is shown by a derivative containing a tetradecyl substituent. When studying the photocatalytic properties of synthesized compounds through the reaction between N-substituted 1,2,3,4-tetrahydroisoquinoline and malonic ester, the mono-tetradecyl deriva-tive demonstrated the best results. According to gas chromatography-mass spectrometry (GC-MS) data, the conversion of the initial heterocycle reached 95%. Therefore, these resulting compounds have the potential to act as an effective photocatalysts.

Several factors contribute to stigma associated with migraine, such as the lack of objective diagnostic tests and biomarkers or traditional gender stereotypes. Patients with migraine can perceive the stigma from different sources: other people (public stigma), organisations (structural stigma) or themselves (internalised stigma), and even neurologists may harbour stigma towards patients with headache. At a social level, repeated migraine attacks lead to restricted participation in work and family life, inducing anxiety, depression and reduced self-esteem. Regarding the workplace, the impact of migraine on their proffesional lives is significant. Individuals who were acquainted with people with migraine at job tended to behave more negatively towards them, and over half of the managers did not consider headache as a valid reason for being absent from work. Effectiveness at work is reduced during a migraine attack, but also because of the interictal symptoms these patients go through. Regarding presenteeism, migraine was the second most expensive disease in the USA, with an annual cost of approximately $240 billion. For all these facts, destigmatisation should take place at the level of healthcare providers, patients and the general population. Implementing educational programmes, training more neurologists in a specialised headache approach, listening to the patients and taking their concerns seriously, or recognizing the disability that the disease causes are strategies that may help minimizing the stigma of migraine.

This study provides an evaluation of a web-based platform for game management and sales, developed using PHP, MySQL, and SASS. The development process utilized the SCRUM methodology, concentrating on essential modules such as user management, game catalog, payment processing, and technical support. The platform's performance was assessed using Google's Lighthouse tool, which evaluates four critical areas: performance, accessibility, best practices, and SEO. The results demonstrated exceptional performance with a score of 99/100 and perfect adherence to best practices with a score of 100/100, reflecting high efficiency and compliance with web development standards. Accessibility received a score of 83/100, indicating potential areas for improvement, while SEO achieved a score of 91/100, suggesting effective search engine optimization. The findings reveal that the platform exhibits high levels of optimization and operational efficiency. However, there remains an opportunity to enhance accessibility features. This study highlights the significance of rigorous web development practices and optimization efforts in achieving superior performance and user experience in web-based systems.

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

Transcranial magnetic stimulation, TMS, has been an innovative method of demure neuromodulation. This paper aims to delve into the different forms of its existence and to evaluate the use of TMS in five psychiatric disorders; Parkinson’s disease, schizophrenia, obsessive-compulsive disorder, major depressive disorder, and major anxiety disorder. Literature pertaining to the use of TMS in treating these specific diseases will be analyzed, and its efficacy and mechanisms will be explored. The literature will be evaluated through sources with established facts and research studies demonstrating function, potency, or inefficacy. TMS poses a method to have a more explicit effect on the regions of the brain, and the exploration of its use in the treatment of psychiatric diseases poses new possibilities in the treatment of psychiatric disorders. Venturing into the options it could have in a sole or combinatorial setting could provide penetrating insight into innovative forms of treatment and research.

The increasing complexity and volume of job listings in the digital era necessitate advanced recommendation systems to match job seekers with suitable opportunities. This paper presents a hybrid job recommendation system that integrates Collaborative Filtering (CF) and Content-Based Filtering (CBF) to enhance recommendation accuracy and user satisfaction. The CF component leverages historical user interactions and similarities between users to suggest jobs, while the CBF component analyzes job descriptions and user profiles to provide personalized recommendations based on individual preferences and qualifications. By combining these two approaches, the proposed system mitigates the limitations of each method and offers a more comprehensive and effective solution. Experimental results on a real-world dataset demonstrate the system's improved performance in terms of precision, recall, and F1-score compared to traditional recommendation models. This study contributes to the field of job recommendation systems by providing a robust framework that effectively bridges the gap between user needs and job market opportunities.

Caudal type homeobox transcription factor 2 (CDX2) is a gastrointestinal cancer biomarker that regulates epithelial development and differentiation. Absence or low levels of CDX2 have been associated with poor prognosis and proposed as a chemotherapy response predictor. Tumour tissue of 668 patients with stage I-IV colorectal cancer were stained for CDX2 and stratified into two subgroups according to expression levels. Statistical tests evaluated CDX2 relationship with survival and chemotherapy response. Of 646 samples successfully stained, 51 (7.9%) had low and 595 (92.1%) had high CDX2 levels. Low CDX2 staining was associated with poor differentiation, presence of lymphovascular or perineural invasion, and more common in colon and right sided tumours. Overall survival (p&lt;0.001) and disease-free survival (p=0.009) was lower in low CDX2 expressing patients. Multivariable analysis validated CDX2 as an independent poor prognostic factor after excluding confounding variables. There was no statistically significant improvement in survival with adjuvant chemotherapy in stage II colon cancer (p=0.11). In the rectal cohort, there was no relationship between CDX2 levels and therapy response. While confirming the prognostic utility of CDX2 in colorectal cancer, our study highlights larger studies are required to confirm utility as a predictive chemotherapy biomarker, especially in left sided and rectal cancers.

In recent decades, there has been a startling rise in the number of cancer patients worldwide, which has led to an amazing upsurge in the development of novel anticancer treatment candidates. On a positive note, arylpiperazines have garnered attention in cancer research due to their potential as scaffolds for developing anticancer agents. These compounds exhibit a diverse array of biological activities, including cytotoxic effects against cancer cells. Indeed, one of the key advantages of aryl piperazines lies in their ability to interact with various molecular targets implicated in cancer pathogenesis. Here, we focus on the chemical structures of several arylpiperazine derivatives, highlighting their anti-proliferative activity in different tumor cell lines. The modular structure, diverse biological activities, and potential for combination therapies of arylpiperazine com-pounds make them valuable candidates for further preclinical and clinical investigations in the fight against cancer. This review, providing a careful analysis of different arylpiperazine deriva-tives and their biological applications, allows researchers to refine the chemical structures to improve potency, selectivity, and pharmacokinetic properties, thus advancing their therapeutic potential in oncology.

In the foodservice industry, clients judge not only the product standard but also the service quality they receive during their restaurant experience. Therefore, when the importance of the dining experience is strongly regarded by consumers in terms of intellectual and motivational attributes, they are happier to suggest the establishment to others. The main objective of this research is to study the influence of different service quality factors in restaurants and their impact on customer satisfaction. This research is also undertaken to evaluate the identification of important factors for the restaurant industry and the levels of service quality among customers satisfaction with the services offered by restaurants in India. As factor analysis, correlation, and regression research, this study was conducted using a modified IBM SPSS Statistics 24 and AMOS instrument among the 630 randomly selected customers. A weighted mean was used to describe the levels of service quality and customer satisfaction. Multiple regressions were used to determine the predictors of customer satisfaction. The results reveal the main 12 service quality factors, which are: 1. Process-related factors: system for take-away orders, order tracking system, and time for food preparation 2. People-related factors: focus on customers' special requests, peer group influence, and making you feel special. 3. Physical Environment: Walking Space, Architecture of the Restaurant, and Signals for Direction 4. Performance-related factors: food served at an appropriate temperature; food presentation; portion size of food in the restaurant. The researchers have developed one model on service quality factors that is described in the research paper.

Ballistic helmets are individual armor equipment designed to protect a soldier's head from projectiles and fragments. Although very common, these helmets are responsible for several causalities due to their low ballistic resistance and large back face deformation to stop the projectile. Therefore, to enhance helmet performance, studies have focused on the development of new materials and new ballistic protection solutions. The purpose of this study was to develop and evaluate a new ballistic solution using thermoplastic-based matrices. The first matrix was based on high-density polyethylene (HDPE). The second matrix was based on HDPE modified with ex-foliated montmorillonite (MMT). The main manufacturing processes of a thermoplastic-based ballistic helmet are presented, along with its ballistic performance, according to the National Institute of Justice (NIJ) standard 0106.01 and an investigation of its failure mechanisms via a non-destructive technique. All the helmets resulted in level III-A ballistic protection. The postimpact helmets were scanned to evaluate the back face deformation dimensions, which revealed that the global cone deformation was deeper in the HDPE than in the HDPE/MMT. The failure analysis revealed an overall larger deformation area in the HDPE and HDPE/MMT helmet delamination zones in regions with a large radius of curvature than in zones with the lowest radius, which is in accordance with previous simulations from the literature.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

International cooperation and food diplomacy play a pivotal role in addressing global challenges to food security. This study explores the effectiveness of Indonesia's international partnerships and diplomatic strategies in bolstering its food security amidst global uncertainties. The research employs a mixed-methods approach, combining qualitative analysis of policy documents and interviews with key stakeholders, alongside quantitative data on food production and trade. Our findings indicate that Indonesia has benefited significantly from technical and financial assistance through collaboration with organizations such as FAO, ASEAN, and bilateral agreements with countries like Japan and China. These partnerships have facilitated the adoption of advanced agricultural technologies, improved infrastructure, and enhanced human resource capacities. However, challenges remain, including bureaucratic inefficiencies and dependency on food imports, which pose risks to sustainable food security. The study concludes that while international cooperation has provided critical support, Indonesia must further streamline its policy implementation processes and reduce its reliance on imports by fostering local production. Strengthening research and development collaborations and actively participating in global forums are also recommended to ensure long-term resilience and food sovereignty. This research provides valuable insights for policymakers and stakeholders aiming to develop effective strategies to navigate the complexities of global food diplomacy and cooperation.

Global change and human activities are altering biodiversity at an unprecedented rate worldwide, leading to sharp declines in global biodiversity and other ecological issues. Over the past 30 years, ecologists have increasingly focused on the question of whether and how the ongoing loss of biodiversity affects ecosystem functioning. This interest has led to the emergence of the field of biodiversity and ecosystem functioning (BEF) as a major research area in ecology. However, historically, researchers have predominantly focused on individual ecosystem functions, neglecting the capacity of ecosystems to provide multiple ecosystem functions simultaneously, known as ecosystem multifunctionality (EMF). This article reviews advances in EMF research, including the selection of functional indicators in EMF studies, different dimensions of biodiversity, the impacts of microbial diversity and biotic interactions on EMF, and drivers of EMF such as non-biological factors. By comprehensively analyzing the overall effects of aboveground biodiversity, belowground biodiversity, and non-biological factors on EMF, the study emphasizes the need to enhance research and application of ecosystem multiserviceability (EMS) approaches.

This study investigates the trends and patterns of robotic-assisted total knee arthroplasty (RA-TKA) from 2016 to 2019. Using data from the Nationwide Inpatient Sample (NIS), we analyzed demographics, prevalence of comorbidities, total cost, and length of stay (LOS) for 88,415 RA-TKA cases identified during this period. Our analysis revealed a significant increase in RA-TKA procedures, from 5,330 in 2016 to 39,495 in 2019. Patients undergoing RA-TKA were slightly younger, with an average age of 66.2 years. The average hospital stay was 1.89 days, and total charges averaged $65,891. We also examined various comorbidities and demographic factors, including race, median household income, and hospital teaching status. The results show that RA-TKA usage in the US has grown substantially, with specific demographic and clinical patterns. Further studies on the long-term outcomes of RA-TKA are needed to fully understand the benefits of this technology.

In this study, we provide the Lie symmetries and a classification of plane symmetric static space-times. Based on the invariance of the Lagrange equations of plane symmetries static space-time systems under the transformation Lie group, we provide the Lie symmetry determination equation, Lie symmetry theorem, and the conserved quantity of the systems; by utilizing the Lie symmetry method to solute the system, we give complete classification of the plane symmetric static space-times systems. The research results indicate that using the Lie symmetry method to study plane symmetric static spacetime can identify a series of conserved quantities that exist in the system; Discovered 5 basic Lie symmetries in the system; When the metric parameters of the system are appropriately selected, the plane symmetric static spacetime can have 6, 7, 8, 9, 11, and 17 Lie symmetric symmetries and conserved quantities.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

This paper discusses the Quantum Temporal Resonator (QTR), a theoretical device leveraging quantum entanglement and metamaterials with negative refraction indices to achieve controlled temporal displacement. By establishing a network of entangled particles within a Bose-Einstein Condensate (BEC) and utilizing a resonant cavity made of advanced metamaterials, the QTR creates localized distortions in space-time. The frequency and amplitude of temporal harmonics within this cavity are finely tuned to allow for precise temporal displacement of matter and information. Numerical simulations demonstrate the coupling between the quantum wave function and electromagnetic fields, validating the theoretical model. The results show distinctive patterns in the wave function and perturbations in the electric field, supporting the feasibility of achieving controlled temporal displacement. This study explores the theoretical framework, mathematical modeling, experimental setup, and potential applications of the QTR, providing a comprehensive analysis of its feasibility and implications.

Keywords: emerging pollutants; pharmaceuticals; plant uptake; soil behavior; soil remediation.

Supplier collaboration and partnership in procurement are essential elements for enhancing organizational efficiency, innovation, and resilience in today's globalized markets. This qualitative study investigates the dynamics of effective supplier relationships, focusing on trust, communication, innovation, quality, cost savings, and resilience as critical factors. Data were gathered through semi-structured interviews, case studies, and document analysis across diverse industries, highlighting insights from procurement professionals and supplier representatives. Key findings underscore the foundational role of trust and communication in fostering successful collaborations. Establishing transparent, ongoing communication channels facilitates mutual understanding, reduces conflicts, and enhances overall satisfaction. Collaborative innovation emerged as pivotal, enabling organizations to pool resources and expertise for product development and process improvement. Quality and reliability were identified as significant outcomes of close partnerships, particularly in industries with stringent standards such as aerospace and healthcare. Furthermore, the study reveals substantial cost savings and efficiency gains through collaborative efforts in process optimization and waste reduction. Effective risk management practices within these partnerships enhance supply chain resilience, enabling organizations to anticipate and mitigate disruptions proactively. Despite these benefits, challenges such as cultural differences, technological integration, and regulatory compliance require strategic mitigation strategies. In conclusion, organizations can optimize supplier collaborations by prioritizing trust-building, fostering a culture of innovation, and investing in robust relationship management practices. This approach not only enhances operational performance but also positions organizations to navigate market uncertainties and achieve sustainable growth in a competitive landscape.

Castanopsis hystrix, a precious tree species in Southeast Asia, has the advantages of rapid growth and high-quality wood materials. However, there are problems such as long breeding cycle, time-consuming and low efficiency, which greatly restricts the industrial development of C. hystrix. To perform the genome selection (GS) for growth and wood traits and the early selection of superior progeny have great significance for the rapid breeding of new superior varieties of C. hystrix. The 226 clones in the main distribution and 479 progenies within 23 half-sib families were used as experimental materials in this study. Genotyping datasets were obtained by high-throughput re-sequencing technology, and GS studies were conducted on the growth (tree height (H), diameter at breast height (DBH)) and wood (wood density (WD), fiber length (FL), and fiber length-width ratio (LWR)) traits. The coefficient of variation (CV) of five phenotypic traits ranged from 10.1% to 22.73%, the average CV of growth traits was 19.93%, and the average CV of wood traits was 9.72%. The Pearson correlation coefficients between the five traits were almost significantly positive. Based on the Genomic Best Linear Unbiased Prediction (GBLUP) model, the broad-sense heritabilities of growth traits were higher than those of wood quality traits, and the different number of SNPs had little effect on the heritability estimation. GS prediction accuracy first increased and then reached a plateau around 3K SNPs for all five traits. The broad-sense heritability of these five traits was significantly positively correlated with their GS predictive ability (r=0.564, P

We estimate general public’s willingness to pay (WTP) for agricultural soil quality protection and improvement in Spain (n=1000) and the UK (n=984) using data from a cross-sectional survey via Qualtrics panels in March-April 2021. We use a double bound dichotomous choice contingent valuation approach to elicit the individuals’ WTP. We investigate the effect of uncertainty on the success of policies aiming at achieving soil protection. In addition, to understand the heterogeneity in individuals’ WTP for agricultural soil quality protection and improvement we model individuals’ WTP through individual’s awareness and attitudes toward agricultural soil quality protection and the environment, trust in institutions; risk and time preferences; pro-social behavior and socio-demographics in Spain and the UK. We found that there is significant public support for agricultural soil quality protection and improvement in Spain and the UK. We also found that the support does not vary significantly under uncertainty of success of policies aiming at achieving soil protection. However the individual’s reasons for supporting agricultural soil quality protection and improvement are found to depend on the level of uncertainty and country. Hence, promoting public support for soil protection needs to be tailored according to the level of general public’s perceived uncertainty and geographic location.

Implementing ethical procurement standards is increasingly recognized as pivotal for organizations aiming to align their practices with principles of social responsibility and sustainability. This qualitative research explores the experiences, challenges, strategies, and impacts associated with ethical procurement across diverse sectors. Motivations for adopting ethical procurement include enhancing corporate reputation, meeting stakeholder expectations, and complying with regulatory frameworks. However, organizations face significant challenges such as balancing ethical considerations with cost-efficiency pressures, navigating complex regulatory landscapes, and ensuring transparency across global supply chains. Strategies for overcoming these challenges emphasize effective supplier relationship management, leveraging technological innovations for transparency, and investing in supplier capacity-building. The study reveals that ethical procurement practices contribute to organizational resilience by improving risk management, enhancing stakeholder engagement, and fostering innovation in procurement processes. Moreover, ethical procurement supports sustainable development goals by promoting environmental sustainability, supporting social equity through fair labor practices, and contributing to economic development. Future research directions include exploring the impact of emerging technologies on ethical procurement, promoting global collaboration for unified standards, and evaluating the long-term sustainability of ethical procurement initiatives.

The daily path of the Sun across longitude yields night and day, but the Sun also travels across latitude on a belt 47° wide. The Sun meridian declination explains the annual budget of natural beam irradiance (NBI), which is the irradiance delivered to the Earth’s surface as a normal projec-tion from the Sun. Data for the sun meridian declination data were obtained from the geometric model. The distribution of NBI was weighed between the Tropics of Cancer and Capricorn. The variation patterns of the solar declination conform to the dynamics of pendular motion. The joint distributions of velocity or acceleration of meridian declination against declination fit circular graphs. The NBI budget fluctuates in inverse proportion to the velocity of declination, yielding 18 sun-paths per degree for latitudes above 20°, and 6 sun-paths per degree for latitudes under 20°. All sites of the planet, whose latitude coincide, whether within or between hemispheres, accumu-late an equivalent budget of NBI.

This qualitative study explores the challenges and successes in supplier development from the perspectives of procurement managers across diverse industries. Through in-depth interviews, key themes emerged, highlighting the critical role of communication, trust, performance measurement, capacity building, risk management, innovation, regulatory compliance, and technology integration in supplier relationship management. Procurement managers emphasized the importance of clear, continuous communication and trust-building activities to align objectives and foster collaborative partnerships. Performance measurement was crucial for evaluating and improving supplier performance, supported by capacity-building initiatives to enhance supplier capabilities. Risk management strategies focused on identifying and mitigating risks, while compliance efforts aimed to ensure adherence to industry standards. Innovation and technological integration were pivotal for driving efficiency and competitiveness, enabling faster time-to-market and improved product quality. Cultural sensitivity and relationship strengthening emerged as essential for navigating diverse cultural contexts and enhancing partnership effectiveness. Overall, the findings underscored positive outcomes including enhanced quality, cost efficiencies, and increased resilience within supply chains. This research contributes valuable insights into effective supplier management practices, emphasizing the strategic importance of supplier development initiatives for sustainable growth and competitive advantage in today's dynamic business environment.

This study explores the intricacies of supplier relationships and inventory optimization within high-technology industries, offering a comprehensive analysis of the strategic imperatives and operational challenges faced by firms in these sectors. By adopting a qualitative research methodology involving semi-structured interviews with supply chain professionals and documentary analysis, the study provides in-depth insights into the evolving dynamics of supply chain management. Findings reveal a marked shift towards collaborative and strategic supplier relationships, characterized by trust, transparency, and joint innovation initiatives. These partnerships are essential for enhancing operational efficiency, driving product development, and navigating market uncertainties. Additionally, the research underscores the critical role of advanced technologies, such as artificial intelligence and the Internet of Things, in optimizing inventory levels and improving supply chain visibility. The integration of these technologies facilitates precise demand forecasting and proactive inventory management, thereby reducing costs and enhancing service levels. However, the study also highlights significant challenges, including geopolitical uncertainties, supply chain disruptions, and cultural barriers, which necessitate robust risk management strategies and adaptive supply chain practices. Strategic implications for organizational leaders and policymakers include the need to invest in supplier development, embrace digital transformation, and enhance risk management frameworks to build resilient and agile supply chains. This study contributes valuable insights into the complex interplay between supplier relationships, inventory optimization, and technological integration in high-technology industries, offering actionable recommendations for practitioners and researchers seeking to drive innovation and maintain competitiveness in a dynamic global marketplace.

The study examines the noise, vibration, and harshness (NVH) management of electric vehicle (EV) powertrains, considering the challenges of the automotive industry's transition to electric drivetrains. The growing popularity of EVs brings new NVH challenges, as the lack of internal combustion engine (ICE) noise makes drivetrain noise more prominent. Key to managing NVH in EV powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces, and high-frequency noise generated by inverters significantly impact overall NVH performance. The article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. Methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics (MBD), finite element method (FEM), and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. The study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components.

Motor imagery requires mental representation of the body, which is obtained by the integration of exteroceptive and interoceptive information. The aim of the study was to investigate the role of interoceptive sensitivity in motor imagery performed through visual and kinesthetic modalities in participants with low (lows, N = 26), medium (mediums, N = 11) and high hypnotizability scores (highs, N = 16), measured by the Stanford Hypnotic Susceptibility Scale: Form A. These groups display different motor imagery abilities and interoceptive sensitivity. The efficacy of motor imagery was measured by chronometric index and self-reported experience, and interoceptive sensitivity by the Multisensory Assessment of Interoceptive Awareness (MAIA) questionnaire. EEG alpha and beta power spectral densities were extracted from recordings acquired during baseline, actual movement, visually and kinesthetically imagined movements. The chronometric indices did not reveal significant differences among groups and between imagery modalities. In contrast, the self-reported imagery efficacy indicated better kinesthetic imagery in highs and mediums than in lows, and no modality difference among lows. MAIA dimensions sustained the differences in sub-jective experience and almost all the EEG differences, slightly different in highs, mediums and lows. This is the first report of a major role played by interoceptive sensitivity in motor imagery.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

Mental Disorders are an important public health problem. The main of this study was to analyze the prevalence of Mental Disorders and Environmental Quality. Data on the Prevalence of Mental Disorders, Gross Domestic Product, and Median Years of Schooling from 178 countries were used. A positive association was observed and was independent of Gross Domestic Product and Median Years of Schooling.

This research discusses the challenges faced by university students in group work assignments and the importance of effective project management. This article introduces Tasknest, a project management platform that helps teams organize tasks, track progress, and improve communication. This article also reviews related studies on project management applications and discusses the Agile methodology used in the development of Tasknest. The implementation of the task management system was done using Agile methods and Typescript programming language. This web-based system includes a landing page, dashboard page, board page, and settings page. Black box testing was conducted to ensure the system functions as expected, with tests on login, board creation, adding columns and cards, and others. Overall, the system was successfully implemented and tested. Tasknest application development includes user stories, acceptance criteria, sprint backlogs, sprint planning, reviews, and retrospectives. System design analysis includes usecase diagram, activity diagram, sequence diagram, and entity relationship diagram. Application features include task list management, team member management, real-time collaboration, notification system integration, task enhancement, in-task checklists, user authentication, UI optimization, performance improvement, collaboration feature enhancement, database design, front-end implementation, and UI design. The Agile Alliance outlines 12 principles for implementing Agile methodologies, which prioritize customer satisfaction, collaboration, and continuous improvement. Agile methodologies offer high flexibility, rapid iteration, responsiveness to change, improved product quality, stakeholder involvement, transparency, and clear responsibilities. Scrum is a framework that supports Agile principles, with a focus on transparency, inspection, and adaptation. Scrum improves team collaboration, rapid adaptation to change, transparency, visibility, product quality, and team motivation. In the development of the Tasknest application, the Scrum methodology is used, with roles such as Product Owner, Scrum Master, and Development Team. Data collection techniques include interviews, observations, and literature studies, while data testing techniques involve black box testing. Planning using Scrum involves creating a Product Backlog with prioritized features and fixes. Prioritization determines the duration of each sprint.