Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, we propose an innovative enhancement algorithm that integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is intro-duced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct convolutional neural network. Finally, the Fourier’s high and low-frequency information of the original image are utilized to fuse different features. To demon-strate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach surpasses other mainstream methods, achieving superior PSNR and structural sim-ilarity index measure (SSIM) metrics and leading to a detection performance improvement of over ten percent.

Leukocyte migration is an essential function of innate and adaptive immune responses. Chemokines and their receptors control the migration system. The abundance of chemokines is controlled by atypical chemokine receptors (ACKRs), chemokine receptor-like molecules that do not couple to the G protein signaling pathways. Among them, ACKR4 regulates dendritic cell migration by controlling the ligands and is involved in tumor development in mouse models. Because no anti-mACKR4 mAb for flow cytometry has been reported, this study aimed to develop a novel monoclonal antibody (mAb) for mouse ACKR4 (mACKR4). Among the established anti-mACKR4 mAbs, A₄Mab-1 (rat IgG_2b, kappa), A₄Mab-2 (rat IgG_2b, kappa), and A₄Mab-3 (rat IgG_2b, kappa) recognized mACKR4-overexpressed Chinese hamster ovary-K1 (CHO/mACKR4) by flow cytometry. The dissociation constant (K_D) values of A₄Mab-1, A₄Mab-2, and A₄Mab-3 for CHO/mACKR4 were determined as 3.5 × 10⁻⁹ M, 6.0 × 10⁻⁹ M, and 1.6 × 10⁻⁹ M, respectively. Furthermore, A₄Mab-1 and A₄Mab-2 could detect mACKR4 by western blotting. These results indicated that A₄Mab-1, A₄Mab-2, and A₄Mab-3 help to detect mACKR4 by flow cytometry and western blotting and obtain the proof of concept in preclinical models.

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real time pricing (RTP) schemes. In this paper, we present a new method to optimise the size and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. The storage in the PHS is used to supply load consumption and/or energy arbitrage. Further, both load supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. The PHS is not worth considering with the present cost of components.

The digital age has ushered in unprecedented connectivity and convenience, but it has also given rise to escalating cyber threats and privacy concerns. This study examines the intricate balance between technological advancement and personal privacy in the face of evolving cyber risks. A comprehensive literature review was conducted, analyzing peer-reviewed articles, government reports, and industry white papers from the past decade. Additionally, case studies of major data breaches and privacy violations were examined. Surveys and interviews with cybersecurity experts, policymakers, and privacy advocates were conducted to gather diverse perspectives. The research reveals a significant increase in both the frequency and sophistication of cyber-attacks, coupled with a growing public awareness of privacy issues. However, there is a notable disconnect between individuals' stated privacy concerns and their online behaviors. Regulatory efforts to protect privacy often lag behind technological advancements and cyber threats. Maintaining equilibrium between technological progress and privacy protection requires a multifaceted approach involving enhanced cybersecurity measures, updated regulations, and improved digital literacy. The study underscores the need for ongoing collaboration between government, industry, and academia to address this complex challenge

The environmental Kuznets curve (EKC) hypothesis posits an inverted U-shaped relationship between economic growth and environmental degradation. However, there is no consensus regarding the EKC hypothesis among countries and regions of different income groups. In this study, we analyze the EKC hypothesis for 158 countries and 44 regions from 1990 to 2020, categorizing them into different income groups based on the World Bank's income classification (low, lower-middle, upper-middle, and high-income countries). Our findings reveal mixed patterns in the EKC across different income levels, highlighting the heterogeneous relationship between economic growth and carbon emissions and showing differentiated patterns among different income levels. While a rise in income for high-income countries should be reflected in a shift toward fewer carbon emissions, for lower-income countries, growth is still associated with rising emissions. This underscores the urgent need for policy interventions that decouple economic development from environmental degradation. A balanced approach to economic growth, which prioritizes carbon neutrality across all stages of development, by tailoring environmental and economic strategies to address CO2 emissions effectively, should be adopted, recognizing the different developmental stages and income levels of countries and regions.

Aiming at the problems of high cost of manual pulling, low reliability of existing pulling devices, high breaking rate and high leakage rate in the process of cotton stalk reuse after leaving the field in Xinjiang cotton area, a loosening soil and root-cutting cotton stalk pulling the setting machine has been researched and designed, established the root-cutting force model, calculated and optimized the key parameters of the V-shaped root-cutting knife, and determined the range of slide cutting angle, cutting edge angle and soil entry angle. The shoveling process simulation of V-shaped root-cutting knife and root-soil complex was constructed, and the working mechanism of V-shaped root-cutting knife was clarified. In order to verify the reliability and operation performance of the V-shaped root-cutting knife, the slide cutting angle, cutting edge angle and soil entry angle were used as the test factors, the response surface test of three-factors and three-levels was carried out with root-breaking force and mean value of the cutting resistance as the test index. The test results were analyzed by variance analysis, the significant of influencing factors on the root-breaking force are in a descending order as: slide cutting angle, cutting edge angle, soil entry angle; the degree of influence on the mean value of the cutting resistance are as follows: slide cutting angle, soil entry angle, cutting edge angle. In order to make the V-shaped root-cutting knife achieve the optimal working state, the parameters of the test index were optimized, and the optimal design parameters of the V-shaped root-cutting knife were set as follows: the slide cutting angle was 48.3°, cutting edge angle was 43.4°, and soil entry angle was 26.2°. The field uprooting test showed that the average pass rate of root breakage was 94.8% and the average pull-out rate of the cotton stalk was 93.2%. This study provides theoretical guidance for the development of root breaking mechanism of cotton straw harvester.

This research aims to explore the integration method of deep learning and large language models in speech recognition to improve the system’s recognition accuracy and ability to handle complex contexts. Deep neural network (DNN), convolutional neural network (CNN), long short-term memory network (LSTM) and Transformer-based large language model are used to build an integrated acoustic and language model framework. Experiments on TIMIT, LibriSpeech and Common Voice datasets show that the ensemble model shows significant improvements in both word error rate (WER) and real-time factor (RTF) compared to traditional models. Especially in terms of adaptability to multiple languages and accent changes, the model shows superior performance. The conclusion shows that through technology integration, the performance of the speech recognition system in complex environments can be effectively improved, providing a new direction for the future development of speech recognition technology.

Human existence and development ever demanded suitable shelter, the dual direc-tion relationship of human-residence have always been a material dealt with to enhance residential living conditions. The emersion of COVID-19 pandemic had introduced ab-rupt and dramatic changes in human life protocols, that exerted clear pressure on differ-ent sectors within the built environment. Housing beard great impact due to needs of so-cial distancing and quarantine obligations to increase chances of human existence. In order to measure human adaptation and residence alterations following new residential requirements, Quality of life investigations to promotse better built environment for oc-cupants had been facilitated using theory of residential dissatisfaction already been adopted in current study. Residents’ responses had been extracted regarding their dissat-isfaction applying Likert scale for measurement and evaluation. The study focused on homogenous housing estates in Erbil City precisely apartments have been selected with different plans and building layouts for their widespread use in the city and have been occupied during the three stages of study to go beyond investigating direct impact of the pandemic towards permanence of alterations and adaptation even after pandemic. Rea-sons causing changed dissatisfaction levels have been investigated for better reliability towards formulating final conclusions and recommendations. Findings showed in-creased dissatisfaction during pandemic in most of spaces, apartment layout and space design significantly affected responses and demands. Levels of dissatisfaction after pan-demic were different from stability to slight decline in dissatisfaction. The effect of limited external spaces in apartments had limited the possibility to manage the pressure, a case that might be less demanding in single family housing due to availability of private gar-dens.

Abstract. Daily vitamin D supplementation using higher than normal dosing (up to the upper limit, UL) and intermittent (once or twice per week) dosing were studied in patients with increased risk of vitamin D deficiency. Using a PubMed database an adequate search for published randomized control trials and other studies was done and results were analyzed. This review provides overview of use of 7000 IU daily, 30,000 IU per week or twice weekly and 50,000 IU weekly of vitamin D on rapid restore of 25(OH)D concentrations to at least 30 ng/mL in different groups of patients at high risk of vitamin D deficiency. The above mention dosing should be considered in adults with obesity or multi-diseased patients on requiring multi-treatment with the use of several drugs, including drugs affecting vitamin D metabolism. The simple schedule for 7000 IU/day, 30,000 IU/week or twice weekly or 50,000 IU/week, all for 6-8 weeks of use by patients representing the increased risk of vitamin D deficiency were provided for consideration. It appeared that higher daily doses or intermittent doses suggested above are effective, safe and responsive for patient’s preferences.

Inertial focusing based Lab-on-Chips represent nowadays a promising technology for cell sorting in various applications, thanks to their coherence with the ASSURED criteria recommended by the World Health Organization, being: Affordable, Sensitive, Specific, User-friendly, Rapid and Ro-bust, Equipment-free and Delivered. Inertial focusing in spiral microchannel can offer a rapid, portable and ease of prototyping solution for cell-sorting. In literature, different geometries of microfluidic channel have been investigated to understand how hydrodynamic forces, due to primary and secondary flow, influence particle focusing. The balancing of these forces in spiral microchannels allows for high throughput and filtration efficiency across different particle and sizes. The understanding of such mechanism represents a key aspect to address the prototyping of spiral devices for the target application. A clear, complete and orderly overview in the current literature is lacking. For this reason, we aim to compare the main features of inertial focusing depending on various cross-section (rectangular, trapezoidal, triangular, hybrid) in spiral devices. Additionally, details about materials and prototyping techniques will be provided. This review study will provide readers a comprehensive overview of current research, guiding future ex-perimentation by expediting the selection of spiral geometries and materials based on the type of particle to be separated.

This study uses simulation and experimental methodologies to explore the efficacy of bi-sectional horizontal kinetic shading systems (KSS) featuring horizontal fins in enhancing daylight comfort across various climates. Given the increasing energy demands and shifting climatic patterns, optimizing daylight levels while minimizing solar heat gain is vital. The paper introduces a bespoke horizontal bi-sectional KSS, simulated in three distinct climates—Wroclaw, Tehran, and Bangkok—using Climate Based Daylight Modelling methods with Ladybug and Honeybee tools in Rhino software. The study employs standard daylight metrics such as Useful Daylight Illuminance (UDI) and Daylight Glare Probability (DGP), along with custom metrics designed to capture the specific dynamics of the bi-sectional KSS. Initial simulations indicated that the KSS significantly improved daylight distribution and uniformity, reducing glare and over-illumination risks by av. 42.34%. Including KSS switching schedules, which are seldom detailed in similar research, enhances the reproducibility and understanding of findings. Subsequently, a physical reduced-scale mock-up of the bi-sectional KSS was tested under real-weather conditions in Wroclaw (lat. 51° N) during June-July 2024. The mock-up utilized two chambers: one equipped with the prototype of bi-sectional KSS and the second without any protection. The operation of fins was guided by two stepper motors controlled by a Python script running on a Raspberry Pi 3 minicomputer. The control chamber served to benchmark the bi-sectional KSS's efficiency by providing results that would be expected if no shading system were installed. The experimental outcomes supported the simulations, demonstrating the KSS's robustness in reducing high illuminance levels to the target comfort level, thus enhancing indoor visual comfort. The values below 3,000 lux were maintained for 68% of the time. However, it must be explicitly stated that during the June-July 2024 heat waves, the illuminance levels inside the test room momentarily exceeded the comfort threshold of 3,000 lux, reaching up to 4,674 lux. Through quantitative and qualitative analyses, the paper advocates for the broader application and further development of KSS as a climate-responsive shading system in diverse architectural contexts.

Condition monitoring is a crucial process for ensuring industrial assets' reliability and operational efficiency. In the age of the digital industry, AI-based data-driven condition monitoring is proving extremely effective in detecting potential issues before they escalate into major problems, thereby reducing downtime, minimizing maintenance costs, and extending the lifespan of the equipment. The availability of tools that can enable the operationalization of these data-driven solutions is, therefore, critical. In this direction, this work proposes a comprehensive, modular, and scalable pipeline covering all the steps from the data acquisition to the AI model training and inference phases. The tool integrates the data acquisition and processing steps with a configurable feature extraction phase. Moreover, the system also integrates deep learning algorithms for diagnosis and prognosis, including a domain adaptation stage to permit transfer learning and increase generalizability. In addition, it features a communication system using MQTT, which allows for an online data stream to enable real-time monitoring and maintenance. The overall infrastructure was deployed in actual industrial settings and tested in a real-time experiment, demonstrating the proposed approach's validity.

The present study aimed to date the death of Korean red pine (Pinus densiflora Siebold & Zucc.) at Sogwang-ri, where is famous for the Korean red pine forests in Korea, using a dendrochronological method, and to investigate the effect of climatic factors on the death and growth decline of the pine trees. For the study, 51 Korean red pines were selected from three stands (Jungmi-gol A, Jungmi-gol B, and Neonam-gol) in the study area, and for the ring-width measurement the increment cores were extracted from the selected trees. The stand chronologies for the Jungmi-gol and Neonam-gol were established using the mean values of individual time series of the living trees from each site, and the chronologies were further used for cross-dating with individual time series of the dead trees to date their death. The abrupt growth reduction was also investigated based on the consecutive ring-width growth decline trend to know the vitality during the living. Results revealed that the red pines at Jungmi-gol A were dead in 2020, at Jungmi-gol B in 2012 or 2013, and at Neonam-gol in 2019. So, it was verified that the death of the red pines at the study sites occurred from high to low elevations. In the analysis of the role of climate in the annual ring growth, the trees at Jungmi-gol A and B located on the southwest slope showed more ring-width growth when precipitation in April, July, and August were increasing, at Neoman-gol located on the north slope when precipitation only in July was increasing. Although the correlations of the ring-width growths at the sites with temperature and precipitation were somehow different from each other, Drought Stress Index (DSI) from May to September at all sites were all negative. Therefore, water stress caused by increased temperature and decreased precipitation is the most significant climatic parameter for the death or growth decline of the pine trees in the study area.

Lecanicillium lecanii KMZW-1, is recognized for its potential as a bio-control agent against agricultural and forestry pests, particularly due to its compatibility with integrated pest management (IPM) strategies. This study aimed to investigate the complete genome of L. lecanii KMZW-1 and assess its pathogenicity against Bactrocera dorsalis. Whole genome sequencing revealed a genome size of 47,239,278 bp, comprising 27 contigs, with a GC content of 51.16%. The genome completeness was assessed as 97.93% using BUSCO analysis, AntiSMASH analysis identified 29 gene clusters associated with secondary metabolite biosynthesis providing insights into the genetic basis of its pathogenic mechanisms and biocontrol potential. The pathogenicity of L. lecanii KMZW-1 was assessed using concentrations ranging from 1×10^4 to 1×10^11 spores/mL. Higher-concentrations (1×10^10 to 1×10^11 spores/mL), resulted in significantly increased cumulative mortality rates of B. dorsalis adults compared to the control group. Notably, the pathogenicity was higher in male adults than in females. Probit regression analysis yielded LogLC50 values as 5.662 for female and 4.486 for male B. dorsalis adults. In summary, L. lecanii KMZW-1 exhibits strong insecticidal activity against B. dorsalis and shows potential as a biocontrol agent with IPM strategies. These findings provide robust genomic evidence supporting the use of L. lecanii KMZW-1in managing against B. dorsalis populations.

Lactate, an important metabolic product, provides energy to neural cells during energy depletion or high demand and acts as a signaling molecule in the central nervous system. Recent studies revealed that lactate-mediated protein lactylation regulates gene transcription and influences cell fate, metabolic processes, inflammation, and immune responses. This review comprehensively examines the regulatory roles and mechanisms of lactylation in neurodevelopment, neuropsychiatric disorders, brain tumors, and cerebrovascular diseases. This analysis indicates that lactylation has multifaceted effects on central nervous system function and pathology, particularly in hypoxia-induced brain damage. Highlighting its potential as a novel therapeutic target, lactylation may play a significant role in treating neurological diseases. By summarizing current findings, this review aims to provide insights and guide future research and clinical strategies for central nervous system disorders.

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This study introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. The findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the Land Use/Cover Change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario1), ecological development priority(Scenario2), and cropland development priority(Scenario3). Climate change scenarios were constructed using CMIP6 data, representing low stress (SSP119), medium stress (SSP245), and high stress (SSP585) conditions. The results show the following: (1) From 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) Water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) By 2030, the water yield of Scenario1 is highest among different land use scenarios, marginally surpassing ecological development priority by 0.5%; (4) Climate scenarios reveal significant disparities, with SSP126 yielding 67% higher water production than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

A Europe-wide outbreak of invasive pediatric Group a streptococcal infections (iGAS) began in fall 2022. Here we report the evolution of GAS hospitalizations in children and adolescents during the second outbreak year in 2023-2024 at a tertiary center in Switzerland. Using prospective monitoring of all in-patient GAS cases below 16 years of age including those with iGAS, we compared case frequencies and clinical characteristics in three time periods (2013-2020; 2022-2023; 2023-2024). Annual GAS hospitalizations increased from a median of 25 case (range 11-28) in 2013-2020 to 89 and 63 cases, respectively, in 2022-2023 and 2023-2024. iGAS cases evolved similarly (2013-2020, 4 cases (3-8); 2022-2023, 32 cases; 2023-2024, 21 cases). The decline in cases from 2022-2023 to 2023-2024 included all types of GAS organ involvement except suppurative infections in the head area, which remained largely unchanged (48 vs. 45 cases). Pleural empyema declined from 13 to 7 cases, possibly explained by a poor overlap of the GAS and influenza curves, respectively, in 2023-2024 compared to 2022-2023. These data document the prolongation of the GAS outbreak into its second winter season in 2023-2024, however with a decline of iGAS, pleural empyema, skin and soft tissue infection and infections of bone, joint and muscle.

This paper, based on the "Unified Theory of Forces," rigorously proves the formula of de Broglie's wave function. This helps us better understand the following important issues: 1) Theoretically prove and understand the essence of de Broglie's wave function; 2) Prove that when the particle's momentum P is determined, the greater the mass of the particle, the more concentrated its wave function distribution, and the weaker the interference effect; 3) Correct the spatial distribution of the wave function of free particles, proving that the wave function of free particles is strictly constrained within a finite spatial range, which is consistent with the facts we observe.

Color distortion in an image poses a challenge for classification and regression when the input data consists of pictures. Because of that, a new algorithm for color standardization of photos is proposed, which serves as a base for a deep neural network regression model. This approach utilizes a custom color template, which was developed based on an initial series of studies and digital imaging. Using the equalized histogram of R, G, B channels from the digital template and its photo, a color mapping strategy is computed. By applying this approach, the histograms are adjusted, and the colors of photos taken with a smartphone are standardized. The proposed algorithm has been developed for a series of photos where the entire surface roughly maintains a uniform color, and the differences in color between the photographs of individual objects are minor. The optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the concentration range of vitamin C from 0 to 87.72 µg·mL-1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL-1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48-2.29 µg·mL-1. The consistency of the predicted concentration results with actual data expressed using R2 was between 0.9956 and 0.9999, for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surface of solutions, which is a common problem in liquid samples. The color matching algorithm has a universal character, and its application scope is not limited.

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network wit user cooperation to migrate the double near-far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Rigorous mathematical analysis shows that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queues stability. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability.

Theoretical physics addresses the fundamental principles of nature and the mysteries of the universe. In the last century, the theory of relativity challenged people's understanding of time and space, while quantum mechanics revealed various forms of energy. However, the concept of gravity remains elusive. All matter in the universe is composed of energy that can be converted into other forms, yet it is always conserved. However, achieving a state of equilibrium, represented by the equal sign, can be challenging to achieve on both macro and quantum scales. A phenomenon that can transition from one state to infinity is called collision and is essential for understanding natural phenomena and physical processes throughout the universe. In the study of general relativity cannot be three-dimensional problems to gain mathematical inspiration, understand the application of new dimensions. Here, we elucidate the nature of gravity, which inspires the solution of the Riemann hypothesis. Our results show that the conduction mode of dimensional energy is defined by Euler's formula. And a special mode of collision is derived which translates into vibration.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

Objectives: This study aimed to investigate whether genetic variations in the OPRD1 gene affect psychopathological symptoms and personality dimensions in eating disorders (ED) patients and/or contribute to ED risk. Methods: The study involved 221 female patients with anorexia nervosa (AN), 88 with bulimia nervosa (BN) and 396 controls. Sixteen tag-SNPs in OPRD1 were identified. Psychometric evalu-ations were conducted using the Symptom Checklist 90 Revised (SCL-90R) and the Eating Dis-orders Inventory Test-2 (EDI-2). P-values obtained by regression models were corrected for mul-tiple testing by the FDR method. Results: In AN patients, genotypes rs204077TT and rs169450TT were linked to lower BMI values (FDR-q=0.035 and 0.017, respectively), as was rs2234918 in a log-additive model (BMI: 18.0±0.28, 17.22±0.18 and 16.59±0.39 for TT, TC and CC carriers, FDR-q=0.012). Additionally, AN patients carrying the rs72665504AA genotype had higher scores in Interpersonal distrust (FDR-q=0.030), whilst BN carriers of rs513269TT and rs2873795TT showed lower scores in Ineffectiveness (FDR-q=0.041 and FDR-q=0.021). In the AN group, BMI correlated with variability in a distal haplotype (rs508448/rs204077/rs223491, FDR-q=0.028), which was also associated with the global PST index of SCL-90R (FDR-q=0.048). Associations were more noticeable in BN patients; again, the distal region of the gene was linked to EDI-2 total scores (FDR-q=0.004-0.048 for the four last haplotypes) and two global SCL-90R indices (GSI: FDR-q=0.011 and PSDI: FDR-q=0.003 for the last s204077/rs2234918/rs169450 combination). No associations with ED risk were observed. Conclusions: Genetic variation in the OPRD1 gene, particularly in its distal region, is associated with BMI and psychopathological comorbidities in ED patients.

Naomi Goldenberg’s model of religion as a “vestigial state” is an excellent interrogation of how religion interacts with the state. To Goldenberg, religions traditionally functioned in the same manner as nation-states before being conquered and delineated as semi-autonomous institutions under the larger secular apparatus. However, I argue in this paper that Goldenberg’s “vestigial state” fails to account for the dynamism that religions have in their relationships to the state. I propose seeing religions as “alternative authorities,” which can be subordinate, cooperative, or hostile to the secular state. These perspectives as an alternative authority are seen best in the evolution of American fundamentalism and Christian nationalism, whose adherents have historically distinguished themselves from the state while simultaneously engaging in the effort to reshape it. In looking at how these movements develop, we understand that religions are constantly evolving in how to achieve and maintain power.

This study investigates the transformation of Bubungan Tinggi, a traditional Banjar house with a high roof, into a modern home through the integration of modern facade design with traditional Bubungan Tinggi facade elements. Bubungan Tinggi is a monumental artifact in South Kalimantan representing the collective memory of the Banjar community with its distinctive typology and anatomy. This study uses a qualitative approach with design experiments to explore the integration of smart home systems, eco-friendly materials, and contemporary design principles. The findings highlight the potential of blending traditional architecture with modern innovations to create sustainable, smart, and culturally sensitive housing solutions.

An online survey assessed the awareness of Nelson region’s residents, comparing responses between those who experienced the installation and those who did not. Findings indicated that eco-art installations can significantly enhance public understanding of ALAN's environmental and health impacts. The installation successfully increased specific knowledge about ALAN, with 92% of visitors reporting learning something new, however overarching knowledge and levels of concern were largely similar between the groups. Emotional engagement was high, with 62% of viewers stating that the installation invoked an emotional response. While the study did not measure behavioural changes, 94% of festival participants found it at least ‘somewhat appropriate’ and 64% ‘very’ or ‘entirely appropriate’ to raise awareness of these issues at such events, suggesting a potential foundation for future action. This research underscores the promise of art-science collaborations in environmental education and public engagement.

Herbicide-resistant weed populations in numerous cropping systems pose a significant threat to global food security by reducing crop yields and increasing production costs. Multiple herbicide-resistant (MHR) Avena fatua L. (wild oat) plants with non-target site resistance (NTSR) mechanisms can express resistance to herbicides without previous exposure and are thus particularly troubling. Although precise molecular mechanisms for resistance to five different mechanisms of action are not clear, numerous transcription factors and stress-related proteins are preferentially subjected to constitutive post-translational modifications (PTMs) in MHR plants. Transient assays to quickly assess if changes to specific genes can change the MHR response will facilitate the development of new strategies to manage MHR weeds. Our study showed that the Barley Stripe Mosaic Virus (BSMV) mediated system can be an option for A sa-tiva (cultivated oat) and A. fatua (wild oat) functional analysis. The inoculation efficiency of this system ranged from 48% to 100%. Its efficacy was demonstrated by successfully knocking down phy-toene desaturase (PDS) expression and overexpressing green fluo-rescent protein (GFP), suggesting its potential for oat functional genomics.

The integration of Artificial Intelligence (AI) in predicting and managing risks for coastal port developments significantly enhances planning and operational efficiency. This paper presents a theoretical framework utilizing AI to predict future scenarios based on geological, hydrodynamic, and oceanographic factors. Focusing on the effective implementation of AI in ports, with a specific emphasis on Bulgarian Black Sea coastal areas such as Nesebur, Sarafovo, Sozopol, St. Anastasia Island, and the region of Karantinata in Varna, the study explores AI applications for site selection, risk assessment, and operational optimization to ensure resilient, efficient, and sustainable port infrastructure.

Clinical academics experience dual workplaces; the clinical and the research environment. However, working in two workplaces leads to difficulty in forming an integrated identity, and hence affects a person’s work environment and well-being. The aim was to explore how clinical academics experience the psychosocial work environment and their suggestions for improving and changing work well-being. A multicenter qualitative approach with group interviews inspired by participatory action de-sign was used. A purposeful criterion sampling strategy was used, selecting researchers affiliat-ed with the university's research network (n=12). Data were analyzed by thematic analysis and the consolidated criteria for reporting qualitative research was applied. The main finding “lack of integration of research in clinical practice”, revealed how clinical academics balanced between the two practices, how their need for belonging in both work environments were established by fellowship, and how motivational factors and role models could be enhanced through a joint commitment of responsibility in research units and clinical practice. This study provides a contemporary understanding of the challenges that clinical academics en-counter and what solutions they would offer. This study adds to knowledge on psychological work well-being and provides solutions on how clinical academics can achieve integration of research in clinical practice.

Functional partially linear regression model, contains a functional linear part and a non-parametric part, in which testing the linear relationship between the response and the functional predictor is of fundamental importance. When functional data cannot be approximated with a few principal components, based on a pseudo estimate for the unknown non-parametric component, we develop a U-statistic of order-two in this paper. Under some regularity conditions, we use the martingale central limit theorem to prove that the proposed test statistic is asymptotically normal. The finite sample performance with simulation studies and real data application are assessed to verify the proposed test procedure.

B-cell lymphoid malignancies is a heterogeneous group of hematologic cancers, where Bruton's tyrosine kinase (BTK) inhibitors have received FDA approval for several subtypes. The first-in-class covalent BTK inhibitor, ibrutinib, binds to the C481 amino acid residue to block the BTK enzyme and prevent the downstream signaling. Resistance to covalent BTK inhibitors can occur through mutations at the BTK binding site (C481S) but also other BTK sites and the phospholipase C gamma 2 (PLCγ2) resulting in downstream signaling. To bypass the C481S mutation, non-covalent BTK inhibitors, such as pirtobrutinib, were developed and are active against both wild-type and the C481S mutation. In this review, we discuss the molecular and genetic mechanisms which contribute to acquisition of resistance to covalent and non-covalent BTK inhibitors. In addition, we discuss the new emerging class of BTK degraders, which utilize the evolution of proteolysis-targeting chimeras (PROTACs) to degrade the BTK protein and constitute an important avenue of overcoming resistance. The moving landscape of resistance to BTK inhibitors and the development of new therapeutic strategies highlight the ongoing advances, that are being made towards the pursue of cure of B-cell lymphoid malignancies.

This case report presents a 63-year-old male patient who developed deep vein thrombosis (DVT) shortly after undergoing retroperitoneal laparoscopic nephroureterectomy for ureteral and renal pelvis tumors. Despite early postoperative mobilization and administration of low molecular weight heparin, the patient developed DVT, highlighting the limitations of current postoperative management strategies. The patient was promptly treated with the placement of an inferior vena cava filter and continued anticoagulation therapy, preventing the progression to pulmonary embolism and leading to a successful discharge.The report emphasizes the importance of vigilant postoperative monitoring and timely intervention in preventing complications such as DVT. It suggests that current strategies, including early mobilization and standard anticoagulation therapy, may not be sufficient to fully prevent DVT, necessitating further optimization of postoperative care. Regular monitoring of D-Dimer levels and ultrasound examinations, as well as appropriate limb protection measures during surgery, are recommended to detect and manage potential DVT early.This case contributes valuable insights into the early detection and management of postoperative DVT, providing useful references for similar cases and stressing the need for enhanced postoperative monitoring and timely intervention.

This small article examines the pivotal roles of redundant and degenerate neural networks in the brain across three critical dimensions: evolutionary development, early childhood neural formation, and their implications in neurological diseases. Redundant networks, characterized by parallel neural pathways with similar functions, provide a foundation for cognitive resilience, ensuring continuity of brain functions despite damage or developmental anomalies. Degenerate networks, comprising structurally varied but functionally equivalent pathways, contribute to cognitive flexibility and adaptability, essential for innovative problem-solving and learning. From an evolutionary perspective, these networks have been instrumental in the brain's adaptation to diverse environmental challenges, facilitating cognitive maximization and the development of complex behaviors. The genetic underpinnings and environmental influences that shaped these networks are explored, highlighting their contribution to the evolutionary success of the human species. In the realm of early childhood development, the formation and reinforcement of these networks are crucial. The brain's plasticity during this period leads to the establishment of robust redundant networks and versatile degenerate networks, shaped significantly by environmental stimuli and learning experiences. This developmental phase lays the groundwork for cognitive abilities and resilience to neurological challenges later in life. Furthermore, the paper delves into the implications of these networks in the context of brain diseases. The disruption of the equilibrium between redundancy and degeneracy is linked to various neurological conditions, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke and Huntington's disease. Understanding the roles and balances of these networks offers insights into the pathophysiology of these diseases and potential therapeutic approaches. This review underscores the significance of redundant and degenerate neural networks in the evolutionary context, their critical formation during early childhood, and their profound impact on brain health and disease. It highlights the need for further research into these networks to enhance our understanding of brain function and to develop targeted interventions for neurological disorders.

Abstract: Cancer, notably breast cancer, is a significant health concern globally, with a substantial burden in Asia, particularly in Pakistan. Late diagnosis exacerbates mortality rates, emphasizing the importance of robust screening programs. This article examines the effectiveness of breast cancer screening initiatives and intervention four years after initial pilot study that spanned 2015-2019, using data from the Islamabad Federal Breast Screening Centre, to assess readiness for national guideline implementation in Pakistan and beyond. Methods: This retrospective cross-sectional study, conducted at the Federal Breast Screening Center (FBSC), involved 5,580 patients undergoing mammography screening over an four-year period from 2015 to 2023. The study examined patients' data, including Breast Imaging Reporting and Data System (BIRADS) categories through double reading mammography and complementary ultrasound (USG) findings. Demographic information, including age, ethnicity, education status, socio-economic factors, breastfeeding history, breast density, and family history, was recorded to analyze its correlation with breast cancer diagnosis and subsequent management. Data analysis was performed using SPSS 2021. Results: The study included a total of 5580 patients who visited the Federal Breast Screening Centre from 2020 to 2023. The mean age of patients attending screenings from 2020 to 2023 was 48.50 ± 9.55 years, with the most common age group being 41-45 years 1,364 (24.4%), followed by 46-50 years 1,008 (18.1%). The ethnicity with the highest frequency was Punjabi 4,647 (83.3%). The distribution of patients across BI-RADS categories was as follows: BI-RADS 0 (n=175, 3.10%), BI-RADS I (n=1,933, 34.60%), BI-RADS II (n=1,691, 30.30%), BI-RADS III (n=642, 11.50%), BI-RADS IV (n=612, 11.00%), BI-RADS V (n=389, 7.00%), and BI-RADS VI (n=138, 2.50%). The prevalence rate of invasive breast cancer during these four years was 10.4%. Conclusion:In conclusion, breast cancer poses an escalating challenge associated with increasing mortality rates. Pakistan, alongside its South Asian counterparts, is witnessing a surge in cases, emphasizing the urgency for comprehensive interventions. Research endeavors such as this provide valuable insights into demographic profiles, aiding in the formulation of tailored guidelines. Moreover, the establishment of additional federal centers for screening is imperative to enhance patient access and mitigate the issue of delayed diagnoses, aligning Pakistan's healthcare infrastructure with that of developed nations.

Urban environments play a crucial role in shaping the lived experiences of women, yet traditional urban design practices often neglect the diverse needs and identities of women, perpetuating social barriers and gender inequalities. This review paper seeks to explore the existing research on how urban design influences women's social challenges in cities. By synthesizing literature from urban planning, architecture, gender studies, and sociology, this review provides a comprehensive overview of the impact of urban design on women's experiences and opportunities in urban spaces. The review highlights gender disparities in urban design, inclusivity in urban planning, and the need for gender-sensitive approaches to address these disparities. Key scholars, including Susan S. Fainstein, Leslie Kern, Sharon Zukin, Daphne Spain, and Vandana Shiva, have contributed significantly to understanding gender disparities in urban spaces and advocating for more inclusive and equitable urban environments. The discussion emphasizes the importance of community engagement, participatory planning processes, and an intersectional lens in urban design to ensure that women's voices and needs are effectively incorporated into decision-making processes. Future research directions include exploring the role of technology, smart city initiatives, and addressing the impacts of gentrification and urban redevelopment on women's access to housing and resources. By advancing research on gender equity in city planning, we can work towards creating more inclusive, equitable, and sustainable urban environments that benefit all residents.

The aim of this article is to identify the best available evidences analyzing liver samples, normall and pathological. In the study were used liver samples, collected during necropsy, from healthy patients and from patients diagnosed with cirrhosis .This mentioned, are known as routinally practice for hepatitis C diagnosis, which conduct to hepatic cirrhosis. In this study direction, chronic liver diseases stimulate a degree of hepatocyte injury. So previously mentioned modifications, alters thet affect the liver architecture and finally ends in cirrhosis are well analyse on microscopic preparates. Folowing morphological steps, it is possible to analyse normal and alterate liver structure, on colored preparates.

Abstract: Pregnancy planning and genetic testing (PPGT) has emerged as a tool in reproductive healthcare, offering parents-to-be insight in their risks of having a child with a genetic disorder. This paper reviews the advantages, drawbacks and challenges associated with PPGT, providing some practical guidance for health care professionals. Advantages include identification of genetic risks, a possibility to informed reproductive decision-making, and the potential to reduce the parents-to-be risk for an affected child. Challenges and drawbacks include provision of service, ethical considerations, genetic counselling complexities, and the need to increase public and professional awareness by comprehensive education and accessibility. Practical guidance involves considerations for selecting appropriate candidates, counselling strategies, and how to integrate PPGT into existing healthcare frameworks. By addressing these factors, PPGT can offer an increased reproductive informed choice for the individual and the couple reducing the burden of disease in the family.

Whilst Model Reference Adaptive Control theory provides mathematical techniques for achieving the performance of the system without undue dependence upon dynamic systems models, its applicability may not be suitable for systems that are crucial to safety due to suboptimal transient and error convergence performance. In this article, our approach enhances the performance of MRAC by utilizing a Generalized Dynamic Inversion (GDI). Two control actions are permitted under the GDI control law. One control is the particular part that is responsible for enforcing the reference system-constrained behaviors. Another control action is carried out by the control law’s auxiliary component and is incorporated into the standard MRAC control law. This augmented part is referred to as a null control vector, and it is used to enhance the performance of MRAC. The classic MRAC control law, when supplemented with a null vector term consisting of an adaptive gain that changes in response to tracking errors, forces the dynamical system to behave similarly to the reference model. This null vector, in particular, restricts the oscillation intensity of the tracking errors, which is employed to generate the adaptive parameters. It is demonstrated that this critical element of our approach enhances transient performance and ensures that error dynamics vanish asymptotically. Extensive simulations have been conducted to prove the efficacy of the proposed method.

When water molecules or other particles collide with protein molecules in a solution, the protein molecules absorb some of the momentum and deform, forming a certain molecular potential energy. When a protein molecule in a high-energy state changes to a stable state, if there is a suitable substrate binding and the potential energy of the protein molecule is greater than the energy required to break the substrate chemical bond, catalytic action will occur. The α-helices and β-folds in protein molecules play a major role in converting the kinetic energy of water molecules into their own potential energy due to the large number of amino acids involved and their regular structure. The internal structural deformation of proteins caused by the impact of water molecules on α-helices and β-folds also has a profound impact on the structure of substrates. ‌

The primary objective of this systematic review study was to investigate the effectiveness, durability, and adverse events of PLLA treatment for aesthetic indications. The search strategy was performed in MEDLINE (Ovid). The electronic literature search of five databases was performed, from the inception of the databases until 12th of February 2024. This was to identify randomized clinical trials that assessed PLLA treatment in adult individuals exhibiting facial aging and/or facial lipoatrophy. Risk of bias was assessed using the Cochrane Risk-of-Bias Tool for Randomized Trials (RoB 2). Eleven RCTs out of 1467 identified citations were included. Four studies showed increased dermal thickness, significant improvement in facial lipoatrophy severity and aesthetic clinical scores, after PLLA treatment with its effects sustained for at least 25 months. Two studies demonstrated the superiority of PLLA over injectable human collagen. Also, three studies showed positive results favoring PLLA when compared with PH gel in lipoatrophy severity, trans epidermal water loss, skin quality, elasticity, and patient satisfaction. All adverse events were mild-to-moderate in intensity, and the main ones worth noting were bruising, hematoma, tenderness, nodules, and edema. Five out of 11 studies were considered as high risk of bias. The evidence on the effectiveness and safety of PLLA for facial rejuvenation is of low quality; thus, the reported high effectiveness, safety, and long-lasting effects for this purpose, should be further investigated.

Petrochemicals and oil refineries are industrial processes that use compounds and polymers derived directly or indirectly from natural gas or crude oil for chemical purposes. They have posed a number of short- and long-term risks to the environment and the people who live nearby. The study aims to determine how the presence of industrial areas, such as oil refineries, affects the local population's perception of their surroundings. The investigation centers on the Tehran Oil Refinery, a major refinery hub, and its perceived environmental impact. For this purpose, a Geographic Information System for Public Participation (PPGIS) covering three basic sections has been designed: socio-demographic information of the participants; self-perceived health; and landscape valuation before and after an intervention. The main observations show a complex interaction between industrial presence and the perception of landscape values. Participants' reactions to the manipulated Photographs reveal important details about the psychological impact of visual elements on emotional perception. There is also a direct relationship between the level of stress and emotional perception in the manipulated and original photos.This research adds to the larger debate about the effects of industrialization on the environment and society. It emphasizes the importance of considering public perception when planning and developing industrial projects. The goal is to strike a balance between industrial operational needs and environmental quality preservation, resulting in long-term urban growth.

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.

Cell aging is associated with the increased production of mitochondrial reactive oxygen species (mtROS) due to mitochondrial dysfunction, and various phytonutrients and estrogens have been shown to improve skin health. Thus, the aim of the current study was to examine damage to dermal fibroblasts by chemically-induced mitochondrial dysfunction and to study the mechanism of the protective effects of carotenoids, polyphenols, and estradiol. Rotenone, a Complex I inhibitor, caused mitochondrial dysfunction in human dermal fibroblasts, substantially reducing respiration and ATP levels, followed by increased mitochondrial and cytosolic ROS, which resulted in apoptotic cell death, increased matrix metalloproteinase-1 (MMP1) secretion, and decreased collagen secretion. Pre-treatment with carotenoid-rich tomato extracts, rosemary extract, and estradiol reversed these effects. These protective effects can be partially explained by a cooperative activation of antioxidant response element (ARE/Nrf2) transcriptional activity by the protective compounds and rotenone, which led to upregulation of antioxidant proteins such as NQO1. To determine if ARE/Nrf2 activity is crucial for cell protection, we inhibited it using the Nrf2 inhibitors ML385 and ochratoxin A. This inhibition markedly reduced the protective effects of the test compounds by diminishing their effect to reduce cytosolic ROS. Our study results indicate that phytonutrients and estradiol protect skin cells from damage caused by mitochondria-generated ROS and, thus, may delay skin aging and improve skin health.

The emergence of mRNA vaccines for SARS-CoV-2 has opened a new page in vaccine development; nevertheless, concerns of the experts have been expressed about unintentional side effects on the gut microbiota (GM). Previous studies showed that this virus acts as a bacteriophage, which infects and destroys specific bacterial strains in the GM. The present manuscript hypothesizes that the synthetic spike protein could create changes in the composition and the functioning of the GM by entering the intestinal cells after vaccination and impairing the symbiotic relationship between intestinal cells and the GM. An experimental protocol to test the hypothesis is suggested.

Camouflage is a natural or artificially process to prevent an object from being detected while camouflage breaking is a countering process for the identification of the concealed object. We report that a perfectly camouflaged object in a two-dimensional scene can be retrieved and detected with stereo-vision assisted three-dimensional (3D) imaging perceived with stereopsis. The analysis is based on binocular energy model applied to general 3D settings. We show that a perfectly concealed object with random noise background can be retrieved with vision’s stereoacuity to resolve the hidden structures. The theoretical analysis is further tested and demonstrated with distant natural images taken by a drone-camera, processed with a computer and displayed in an autostereoscopy. The recovered imaging is presented with removal of the background interference to demonstrate the general applicability for camouflage breaking with stereo imaging and sensing.

The Asia-Pacific region contains almost half of the planet’s coastal carbon-sequestering (blue carbon) environments (mangroves, salt marshes, seagrasses). These habitats are highly valuable to sequester carbon and mitigate climate change, but are under threat throughout the region, due in significant part to an insufficient policy environment to protect, restore, or create new coastal wetlands/blue carbon environments. Using a systematic evidence synthesis we reviewed the existing literature on blue carbon policy, and found that gaps and challenges fall under three themes, “Land Tenure and Ownership”, “Funding and Protecting Blue Carbon Habitat”, and “Conflicting Priorities and Jurisdictions”, with two overarching issues, the inconsistent recognition of rights of indigenous peoples and local communities (IPLCs) and women, and sea-level rise. This study makes recommendations to ensure high-quality blue carbon projects through improving equitable outcomes, particularly for women and IPLCs, and to reduce barriers to implementing blue carbon projects. These recommendations include gender sensitive and participatory mapping along with a registry of ownership with extensive gender equitable IPLC consultation to resolve ownership uncertainties. Recommendations for community level and gender equitable benefits beyond individual land owners are important to address the inequity of land ownership. Where land ownership changes under sea-level rise and intentional barrier removal, carbon rights should be retained by the landowner; otherwise, there is little incentive to undertake blue carbon projects. Due to the limited income that results from some carbon credit payments, other crediting and non-market methods can be considered to supplement landowners’ incomes and encourage habitat protection. Conflicting priorities between authorities responsible for managing coastal wetlands can be resolved through systematic restructuring/streamlining and collaborative workshops. Some will require changes to policies and amending delegated legislation.

The commercial aviation industry is constantly evolving, driven by technological innovation and the growing demand for more efficient and environmental friendly transportation solutions. In this context, the design and development of next-generation aircraft is a crucial task, with the goal of reducing environmental impact and improving flight efficiency while continuing to ensure passenger comfort and safety. This paper aims at evaluating the technical feasibility as well as economic and environmental viability of a narrow body aircraft powered by hydrogen for direct combustion. Notably, the paper offers innovative insights regarding the update of literature models for conceptual design, in order to consider the impact of hydrogen fuel on the sizing loop at aircraft level. Moreover, it also focuses on the evaluation of operating costs for the aforementioned aircraft class, highlighting the required model updates and considerations to properly match the effect on costs of the introduction of the innovative fuel. Ultimately, a preliminary analysis of upstream sustainability of the concept, considering fuel life cycle, is provided, in order to understand the overall competitiveness, not only in terms of monetary resources but also of environmental impact, beyond the pure operating life of the airplane.

Cocoa bean shells (CBSs) represent the external layer of cocoa beans. The shells are removed during the winnowing after the pre-roasting, a processing step of chocolate production; in in the past they were considered a waste. CBSs possess many interesting nutritional characteristics in both macro and micronutrients. CBSs result very rich in dietary fiber and antioxidant compounds such as phenolic acids and flavan-3-ols. Soluble dietary fibers are potentially associated with fermentability and prebiotic activity on human gut microbiota instead antioxidants compounds have been linked with positive effects on human health. Based on these properties, the aim of this study was the valorization of CBSs thorough enzymatic treatments. The shells were treated both raw and defatted; three sets of carbohydrases have been used in order to destructure the fiber profile, potentially enhancing the potential prebiotic activity of CBSs. Cellulase, xylanase and pectinase were used to perform enzymatic treatments. After the enzymatic hydrolisis, both soluble and insoluble fibers fraction were quantified, in parallel with the total phenolic and the total flavonoid contents and the antioxidant activity. Moreover, the HPLC-PDA analysis was carried out for the main compounds of CBSs. The enzymatic treatmens allowed to obtain an enhancement in soluble fibers, for both defatted and raw CBSs, particularly using cellulase, xylanase and their combinations. Finally, a parallel preservation of the antioxidant capacity and the content of the main bioactive compounds after the treatments was observed. These results can be used to exploit the enzymatic treated CBSs to further industrial applications in the food and nutraceutical fields.

Memory decline is considered a normal part of aging, while the relationship between melatonin deficiency and cognitive function is complex and not fully understood. The present study investigated the role of melatonin deficiency functioning as a hormone at different ages on working and short-term recognition and spatial memory in rats. An age-related decline in memory function was tested in the Y-maze, the object recognition test, and the radial arm maze. Aging was associated with reduced expression of brain-derived neurotrophic factor (BDNF) throughout the hippocampus, whereas pinealectomy exacerbated this process specifically in the CA3 region of 3- and 14-month-old rats. The region-specific reduced expression of the extracellular signal-regulated kinase (ERK)1/2 and pERK1/2 was observed in young adult rats with pinealectomy. However, in middle-aged rats, the expression of these signaling molecules was either downregulated or upregulated in different hippocampal regions. The reduced ratio of pCREB/CREB in the frontal cortex and hippocampus was associated with memory impairment in young adult and middle-aged melatonin-deficient rats, whereas old rats were unaffected. Our study provides insights into the molecular pathways involved in age-related memory changes associated with melatonin deficiency, highlighting the importance of the BDNF / ERK1/2 / CREB pathway in the hippocampus and suggesting a critical period for intervention.

Chronic granulomatous disease (CGD) is a group of rare primary inborn errors of immunity characterised by a defect in the phagocyte respiratory burst, which leads to severe and life-threatening infective and inflammatory complications. Despite recent advances in our understanding of the genetic and molecular pathophysiology of X-linked and autosomal recessive CGD, and growth in the availability of functional and genetic testing, there remain significant barriers to early and accurate diagnosis. In the current review, we provide an up-to-date summary of CGD pathophysiology, which underpins methods of diagnostic testing for CGD and closely related disorders. We present an overview of the benefits of early diagnosis and when to suspect and test for CGD. We discuss current and historical methods for functional testing of NADPH oxidase activity, as well as assays for measuring protein expression of NADPH oxidase subunits. Lastly, we focus on genetic and genomic methods employed to diagnose CGD, including gene-targeted panels, comprehensive genomic testing and ancillary methods. Throughout, we highlight general limitations of testing, and caveats specific to interpretation of results in the context of CGD and related disorders, and provide an outlook for newborn screening and the future.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of professional nurses in maintaining service standards under new governance.We conducted the study at sub-district health-promoting hospitals using a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes. We collected data from 340 nurses and 400 patients using structured questionnaires. We divided the questionnaires into seven sections, which captured a range of metrics such as the personal data of nurses, hospital service activities, and leadership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality. We used paired t-tests to compare performance metrics before and after the administrative transfer, one-way ANOVA to assess variability among different service units, and binary logistic regression to identify key determinants of service quality in our statistical analysis. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited minimal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities. This could contribute to improved healthcare delivery across restructured health systems.

Candida spp. is rarely found in the etiology of neonatal early-onset sepsis (EOS). However, candidemia is associated with increased mortality and morbidity, as in late-onset sepsis. Congenital candidiasis may present as mucocutaneous infection or, more rarely, as a systemic infection in term and preterm infants. Material and methods: Case reports of two cases of congenital systemic candidiasis (CSC) produced by Candida albicans and a review of the data in the literature. Results: Both neonates were male, born vaginally, with risk factors for congenital candidiasis. One of the infants was born at term and presented with an almost generalized maculopapular rash at birth; C. albicans grew in the blood culture sampled at 36 hours; parenteral fluconazole was started immediately, and no complications were noted. The other infant was born preterm, at 28 weeks of gestation, and presented signs of respiratory distress on the 4th day of life after initial successful treatment of the respiratory distress syndrome with surfactant and non-invasive respiratory support. C. albicans was present in the neonatal blood and gastric aspirate cultures, and in the maternal vaginal cultures (all sampled at birth); parenteral fluconazole therapy was administered for 30 days as investigations revealed both liver and renal involvement and the infant development bronchopulmonary dysplasia. Conclusion: As data in the literature suggests, despite occurring at any gestational age, CSC may present various clinical pictures, the rash may mimic many other neonatal conditions, and the systemic dissemination cannot be distinguished from bacterial EOS. Careful anamnesis and a high index of suspicion are important for the prompt recognition and treatment of CSC, optimizing the short- and long-term outcomes.

The Mediterranean Corn Borer (MCB), Sesamia nonagrioides, poses a significant threat to maize crops, necessitating effective pest management strategies. This study investigates the compatibi-lity of two entomopathogenic nematode (EPN) isolates, Steinernema feltiae KV6 and Heterorhabditis bacteriophora EO7, with four registered insecticides for MCB control: deltamethrin, flubendiamide, spinetoram, and betacyfluthrin. The impact of these insecticides on EPN mortality, infectivity, and reproduction was assessed. Results indicate that deltamethrin exhibits the lowest toxicity to EPNs, with mortality rates of 1.3% for S. feltiae and 0.63% for H. bacteriophora at field dose (FD) after 24 hours, and 4.63% and 1.96% respectively after 48 hours. In contrast, spinetoram and flubendiamide showed higher toxicity, with mortality rates of 38.04% and 14.17% for S. feltiae at FD after 48 hours. The infectivity assays demonstrated that deltamethrin-treated EPNs caused up to 100% mortality in MCB larvae, while the reproduction capacity varied significantly between the EPN species and insecticides. H. bacteriophora exhibited higher progeny production, especially in the presence of Deltamethrin (87,900 IJs/larva). The findings suggest that integrating EPNs with selective insec-ticides like deltamethrin can enhance pest control efficacy and support sustainable agricultural practices. This study provides valuable insights for developing integrated pest management (IPM) strategies aimed at mitigating MCB infestations in maize while minimizing environmental im-pacts.

It has been discovered that representing odd integers as modified binary expressions ending with $2^m - 1$ for $m \geq 1$ helps in understanding the dynamics of Collatz-type sequences. Starting with the original Collatz sequence $3n + 1$, it is found that when the odd step is applied to an odd integer ending with $2^m - 1$, an even integer that ends in $2^{m+1} + 2^m - 2$ is obtained, which is exactly once divisible by 2, unless the lowest index reduces to zero. This implies that the sequence alternates between odd and even steps $m$ times. This governs the dynamics of the Collatz-type sequences because the value of $m$ determines the number of times the integer can be divided by 2 in each even step. A shortcut method is then presented that gives the even integer after $m$ odd-even steps are completed. This formulation also allows construction of odd integers to follow specific patterns of odd and even steps. The shortcut method for the modified Collatz sequence $5n+1$ is also presented.

Considerable efforts have been devoted to modifying gravity, which aim to elucidate the possible existence or nature of dark matter and energy, describe observational data more effectively, and formulate quantum gravity. In addition, despite the immense success of the quantum field theory, the framework requires renormalization techniques and breaks down at high energies. Recently, the Planck legacy 2018 release has confirmed the existence of an enhanced lensing amplitude in the cosmic microwave background power spectra, which prefers a positively curved early Universe with a confidence level higher than 99%. This study considers the implied curvature of the early Universe as the curvature of “the background or 4D conformal bulk as a manifestation of vacuum energy” and distinguishes it from the localized curvature that is induced in the bulk by the presence of celestial objects that are regarded as ”4D relativistic cloud-worlds.” Analogously, because gravity appears to emerge owing to spacetime curvature and does not exhibit critical characteristics shared by other fields, it has been incorporated as the local curvature of the bulk affecting the embedded quantum fields that are regarded as propagating “4D relativistic quantum clouds.” To consider the effects of the bulk on embedded clouds, this paper presents interaction field equations in terms of brane-world modified gravity and the perspective of geometrization of the quantum mechanics wherein gravity is manifested by the curvature of 4D conformal bulk as an indicator of the field strength of vacuum energy on the embedded 4D relativistic clouds in addition to the boundary interactions, which could remove the singularities and satisfy a conformal invariance theory. A visualization of the evolution of the 4D relativistic cloud-worlds over the conformal spacetime of the 4D bulk is presented, whereas the standard said theories can be recovered from the interaction field equations.

Climate change is an important threat to orchard production at Mediterranean basin. Diversification seems to be implemented at a farm level as an important strategy to face these challenges. This paper aims to analyse farmer’s perceptions on crop diversification as a way to enhance farm’s adaptive capacity in a context of current and future climate changes impacts. Our case study is focused in orchards growers from French department of Vaucluse recently diversifying their activities through pistachio (Pistacia vera L.). We did in-depth semi-structured farmers’ interviews in 2021. The results show that diversification is supposed to enhance 3 main components of farms adaptative capacity: (1) strengthening robustness through improvement of buffering capacities to climate impacts (trough robust and adapted plants), economic uncertainties (by exploring local/niche markets) and labour scarcity (by lowering workforce requirements and mechanization); (2) Learning by experimenting, monitoring and exchanging with pairs and advisors (3) Cope with change related with societal demands and helps to increase personal satisfaction. Finally, promote farm’s diversification can contribute to enhance farm’s adaptive capacity under climate change impacts.

Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using an intelligent (i.e., model-based and optimization-driven) approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer; (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic; and (4) improving SmartScan's computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation and 50% in geometric inaccuracy were observed using SmartScan, without significantly sacrificing print speed. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

We have shown that Newton’s third law does not strictly hold in a system with remote elements, due the finite speed of signal propagation and thus force imbalance occurs at the system’s center of mass. As the said system is affected by a total force for a finite duration, mechanical energy and momentums are gained by the system. In early works we assumed that the bodies were macroscopically charge neutral. Later we removed this restriction, thus analyzing the consequences on a possible electric charged relativistic motor. On the first published paper on this subject we studied this phenomena in general but gave only an example of a system reaching a stationary state, in this paper we shall analyze a charged retarded electromagnetic motor in a more general time dependent setting giving specific examples in which the system never reaches a stationary state yet produces steady linear momentum non the less.

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targets Ahmedabad city in India and employs the Seasonal Auto Regressive Integrated Moving Average with eXogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using the Augmented Dickey-Fuller test, and the Akaike Information Criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and predictive accuracy. The model achieved an RMSE of 1.0265, indicating high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model’s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model's applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further.

Biomass can be converted into energy/fuel by different techniques, such as pyrolysis, gasification, and others. In the case of pyrolysis, biomass can be converted into a crude bio-oil around 50-75% yield. However, the direct use of this crude bio-oil is impractical due to its high content of oxygenated compounds, which provide inferior properties compared to those of fossil-derived bio-oil, such as petroleum. Consequently, bio-oil needs to be upgraded by physical processes (filtration, emulsification, among others) and/or chemical processes (esterification, cracking, HDO, among others). In contrast, HDO can effectively increase the calorific value and improve the acidity and viscosity of bio-oils through reaction pathways such as cracking, decarbonylation, decarboxylation, hydrocracking, hydrodeoxygenation and hydrogenation where catalysts play a crucial role. This article first focuses on the general aspects of biomass, subsequent bio-oil production, its properties, and the various methods of upgrading pyrolytic bio-oil to improve its calorific value, pH, viscosity, degree of deoxygenation (DOD), and other attributes. Secondly, particular emphasis is placed on the process of converting model molecules and bio-oil via HDO using catalysts based on nickel and nickel combined with other active elements. Through these phases, readers can gain a deeper understanding of the HDO process and the reaction mechanisms involved. Finally, the different equipment used to obtain an improved HDO product from bio-oil is discussed, providing valuable insights for the practical application of this reaction in pyrolysis bio-oil production.

This paper explores the feasibility of applying Bayesian statistical methods to study the distortion patterns induced by carburizing heat treatment. By establishing posterior and predictive distribution models for torsion bar dimensions, we aim to accurately understand and predict the expansion behavior, thus enhancing control over carburizing-induced dimensional changes. Bayesian methods allow for the integration of prior knowledge and real-time data, providing a more comprehensive understanding of the distortion phenomena. This approach not only improves the precision of dimensional predictions but also contributes to optimizing the overall manufacturing process, ensuring that the torsion bars meet the rigorous standards required for high-performance applications in demanding industrial environments.

Current fertilizer recommendations often neglect nutrient cycling across crop rotations. This study aimed to assess the decay rate and nutrient (N, P, K) release patterns of sorghum, black oat, and corn residues and their effects on corn yield in an integrated crop-livestock system. The experiment used factorial treatments based on two sward heights (high and low) and two nitrogen fertilization levels (N-Pasture at 200 kg N ha-1 and N-Corn at 0 kg N ha-1. Litter bags were collected at various intervals from each crop to measure nutrient release patterns and decomposition rates. The results showed that pasture height and nitrogen fertilization significantly influenced decomposition and nutrient release, affecting the subsequent grain crop phase. Potassium was released rapidly and in high amounts. Nitrogen fertilization during the pasture phase prevented nitrogen and phosphorus immobilization in black oat residue and reduced immobilization in corn residue.These findings highlight the importance of accounting for nutrient cycling and decomposition rates in fertilization strategies to enhance the sustainability of integrated crop-livestock systems.

The present research project entitled "Development of a method for predicting the optimal maturity level of avocado using machine learning" aims to establish an accurate and efficient approach to assess the maturity of avocados using machine learning methodologies. This research specifically focuses on identifying the relevant physical and chemical characteristics of avocados, creating a dataset containing categorized images of maturity levels, and creating machine learning models capable of accurately predicting fruit maturity. The studies reveal that the application of machine learning, in particular convolutional neural networks and multisensor models, has the potential to transform the prediction of avocado maturity and thus improve product quality and customer satisfaction. The findings indicate that the proposed techniques achieve an accuracy rate of over 90%, demonstrating their viability for integration into mobile applications that can benefit growers and suppliers in their decision-making processes.

In the realm of molecular science, intricate relationships between molecular structures and their biomedical and pharmacological characteristics have been revealed through empirical experiments. This exploration hinges on the application of numerical descriptors, known as Topological Indices, which illuminate the inherent properties of diverse molecular structures. With particular significance in the medical and pharmaceutical domains, these indices facilitate the prediction of biological features for new chemical compounds and drugs by quantifying weighted entropies. In the context of this paper, we delve into the concept of graph entropy, weaving it intricately with the topological properties of the crystalline framework of the copper oxide molecule, denoted as Cu\textsubscript{2}O$[i,j,t]$. Our primary objective is to unravel the mathematical symphony underlying the structural intricacies of Cu\textsubscript{2}O$[i,j,t]$ and to imbue it with the conceptual essence of entropy. We accomplish this through the computation of entropy, leveraging various topological indices, including weight. In addition to our analytical journey, we present a graphical comparison that juxtaposes the computed indices and entropies, shedding light on the interplay between mathematical analysis and the structural elegance of Cu\textsubscript{2}O$[i,j,t]$. This endeavor contributes to a deeper understanding of the material's multifaceted applications, spanning domains from chemical sensors to solar cells, photocatalysis, and batteries, where Cu\textsubscript{2}O's crystallographic structure plays a pivotal role.

Monte Carlo simulations were conducted to investigate the interaction between nucleation and the rates of chemical reduction in Au/Ag, Au/Pt and Au/Pd nanoparticles prepared in microemulsions using a one-pot method. The impact of nucleation on final nanostructure depends on the critical nucleus size value: at a high critical nucleus size, nucleation becomes the main factor in determining the final nanostructure, even with very large difference in reduction rates, as seen in the Au/Pd pair. However, when the critical nucleus size is small, the difference in reduction rates of the two metals becomes the key parameter determining the final nanostructure. Furthermore, the relevance of heteroatomic nucleation on the mechanism of nanoparticle formation depends on the difference between the reduction rates of the two metals. Smaller differences, such as in the Au/Ag or Au/Pt pairs, result in a greater impact of heteroatomic nucleation on the final nanostructure. In contrast, in the Au/Pd pair, heteroatomic nucleation becomes less important due to the low availability of Pd until late stages of synthesis. This study provides deeper insight into the complex mechanisms that govern reactions in microemulsions.

Contrasting results have suggested that the prevalence and intensity of post abortion emotional distress (PAD) either diminishes rapidly or persists over time. Few studies have followed women for longer than five years. This study examines retrospective population data on 226 ever aborting U.S. women aged 41-45, at an average of 20 years following the abortion, with extensive measures of PAD. Most post abortive women (55.2%) experienced little or no distress from their abortion(s) and 74.6% were confident it had been the right decision, but 44.8% reported moderate (20.7%) to high (24.1%) distress related to their abortion(s), including 37.7% who reported negative emotions; 31.2% “frequent feelings of loss, grief or sadness;” 24.6% “frequent thoughts, dreams, or flashbacks;” 23.2% for whom thoughts and feelings about their abortion “negatively interfered with life, work, or relationships.” Time since the abortion (mean 20 years, range 0-31 years) was not correlated with PAD (r = -.02, p .71). For the minority of women who experienced it, PAD did not diminish but persisted for decades. An estimated 7.5 million U.S. women currently experience persistent high PAD. Further research to specify risk factors and possible interventions for postabortion distress would be of value for this group of women.

Bark beetles are one of the major causes of tree mortality in coniferous forests worldwide. The large-scale disturbances by beetle outbreaks have strongly increased in Honduras and other countries, leading to significant ecological, economic, and social consequences. However, the taxonomy, local distribution, and environmental aspects of most bark beetles and their biological predators remain poorly studied. Here, we applied a morphological approach to identify Scolytinae individuals across different forest areas in Honduras. Moreover, the barcoding approach was used by amplifying and sequencing the mitochondrial COI gene. The intraspecific genetic diversity of Ips apache was also analyzed. 1,131 individuals were examined, and 27 genera were identified. Most of the specimens were identified as belonging to the genus Ips, accounting for 53.2% of the total. Xyleborus accounted for 16.5% and Temnoscheila accounted for 10%. Less than four individuals were found for fifteen genera. Ips, Temnoscheila, Xyleborus, Hypothenemus and Pityophthorus exhibited the most extensive geographic distribution among the sampled sites. At the genus level, Olancho, El Paraíso, and Copán displayed the highest diversity. This study is the first report of two genera (Xylomeira and Stephanopachys) in Honduran pine forests. Among individuals of I. apache, there was evidence of intraspecific genetic diversity, but no population structure was detected. While our findings update the list of bark beetles and biological predators associated with Honduran coniferous forests, more taxonomic inventories and ecological studies are required to understand the spread of bark beetles in pine forests.

Classification of pre-processed fMRI dataset using the functional connectivity(FC) based features is considered a challenging task due to high dimensional FC features set and smaller dataset size. To tackle this specific FC high dimensional features set and a smaller dataset size , We have proposed here a GAN based dataset augmenter to firstly train the GAN on the NYU Connectivity Features dataset and used the trained GAN to generate synthetic features per category and after getting sufficient number of features per category , A multi-head attention mechanism was used as a head for the classification . We name our proposed approach as "ASD-GANNet" which is End-To-End and does not require hand-crafted features as the multi-head attention mechanism focuses on the features that are more relevant . Moreover , We compared our results with the 06 available state-of-the-art techniques from the literature and Our proposed approach results using "NYU" site as a training set for generating GAN based synthetic dataset are promising. We achieved an overall 10-fold cross validation based accuracy of 82% ,sensitivity 82% and specificity 81% outperforming the available state-of-the art approaches. Sitewise comparison of our proposed approach also outperformed the the available state-of-the-art as out of the 17 sites our proposed approach has better results in the 10 sites.

The Aether Physics Model (APM) presents a radical reinterpretation of fundamental physics, offering potential solutions to long-standing problems in Quantum Mechanics and General Relativity. This paper explores the core concepts and equations of the APM, demonstrating how they provide a unified framework for understanding the nature of space, time, matter, and fundamental forces. We examine the implications of the APM's quantized Aether structure, the role of the Singularity and Gforce, and the reinterpretation of fundamental constants and forces. The model's predictions regarding dark matter, neutron content in stable matter, and the unification of forces are discussed, along with potential experimental tests. Additionally, we introduce the loxodrome concept and toroidal particle topology as central elements in explaining fundamental particles and forces. While highly speculative, the APM offers a thought-provoking alternative to standard physics models and warrants further investigation.

Over the last decades, in addition to the improvement of pathophysiological knowledge regarding the role and mechanisms of action of Vitamin D, there has been a progressive advancement in analytical technologies for its determination, as well as in methodological standardization. A significant number of scientific works, meta-analyses, and guidelines have been published on the importance of Vitamin D and the need for supplementation in deficient individuals. However, it appears necessary to clarify the fundamental elements related to the determination of Vitamin D (both at the strictly analytical and post-analytical levels) and the scientific evidence related to the efficacy/safety of supplementation. Additionally, given the important interrelations between Vitamin D, parathyroid hormone (PTH), and fibroblast growth factor-23 (FGF23), the analytical issues and clinical utility of these biomarkers will be discussed.

Drone technology is undoubtedly part of our everyday life these days. The business and industrial use of unmanned aerial vehicles (UAV) can provide favorable solutions in many areas of life, and they are also great for emergency situations and for reaching hard-to-reach places. However, there are many technological and regulatory challenges to overcome in their application. One of the weak links in the operation of UAVs is the limited availability of energy. In order to address this issue authors develop a novel trajectory planning method for UAVs to optimize the energy necessity during flight. For this, an “energy map”, which serves as the basis for trajectory planning, must first be prepared, i.e., the energy consumption of the individual components must be determined. This is followed by configuring the 3D environment including partitioning of the work space (WS), i.e. the definition of free spaces, occupied spaces (obstacles) and semi-occupied/free spaces. On the basis of the partitioned space, the corresponding graph-like path(s) has to be generated, where a graph search-based heuristic trajectory planning can be started, taking into account the most important wind conditions, e.g. velocity and direction. Finally, in order to test the theoretical results, some sample environment will be created, where the proposed path generations will be tested and analyzed. The suggested method is able to find the optimal path in terms of energy consumption.

In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use land cover (LULC) classes and crop identification. Applying Remote Sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/ algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resources management. The (this)study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/ classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.

Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.

Any homeostatic protometabolism would have required orchestration of disparate biochemical pathways into integrated circuits. Extraordinarily specific moieties were also required. Assembly Theory conflated with its cousins—Complexity Theory, Chaos theory, Quantum Mechanics, Irreversible Nonequilibrium Thermodynamics and Molecular Evolution theory— collectively have great naturalistic appeal in hopes of their providing the needed exquisite steering and controls. They collectively offer the best hope of circumventing the need for active selection required to formally orchestrate bona fide formal organization (as opposed to the mere self-ordering of chaos theory) [1]. This paper focuses specifically on Assembly Theory’s contribution to life-origin models.

Indole-3-acetic acid (IAA), as an important regulator of potato growth, seriously affects the growth and yield of potato. Although many studies have reported that IAA-producing Bacillus can promote plant growth, little research has been done on its synthesis pathway and the molecular mechanisms. In this study, IAA-producing strain WL35 was identified as Fitibacillus barbaricus, and its yield was 48.79 mg·L-1. The results of the pot experiments showed that WL35 significantly increased plant height, stem thickness, chlorophyll content and number of leaves of potato plants by 31.68%, 30.03%, 32.93% and 36.59%, respectively. In addition, in the field experiments, WL35-treated plants increased commercial potatoes yield by 16.45%, vitamin C content by 16.35%, protein content by 75%, starch content by 6.60%, nitrogen, phosphorus and potassium accumulation by 9.98%, 12.70% and 26.76%, respectively. Meanwhile, the synthetic pathway of WL35 was found to be dominated by the tryptophan-dependent pathway, the IAM, TAM and IPA pathways worked together, and the pathways that played a role at different times were different. Furthermore, RNA-seq analysis showed that there were a total of 2875 DEGs regulated in the samples treated with WL35 seed dressing compared with the CK, of which 1458 genes were up-regulated and 1417 genes were down-regulated. Potato roots express differential genes enriched in processes such as carbohydrate metabolism processes and cellular polysaccharide metabolism, which regulate potato plant growth and development. The above results provide a theoretical basis for the further exploration of the synthesis pathway of IAA and its growth-promoting mechanism on potato.

Metabolic engineering in plants offers powerful strategies to enhance crop yield and quality, addressing global challenges in agriculture and food security. This study explores cutting-edge approaches in manipulating primary and secondary metabolic pathways, utilizing advanced genetic modification tools to improve key traits in crops. We examine successful applications that have enhanced photosynthetic efficiency, nutrient use, stress tolerance, and the production of valuable metabolites. The study also delves into emerging trends, such as synthetic biology approaches and multi-gene trait stacking, which are revolutionizing the field. By integrating omics technologies and computational modeling, researchers are optimizing metabolic engineering designs with unprecedented precision. As the field rapidly evolves, we consider the regulatory and biosafety aspects of genetically modified crops, providing insights into the future of sustainable agriculture and crop improvement.

The objective of the described activity is to develop technologies or proposals that can be implemented within the cycle to enhance the relationship between climate change, water, energy, and food. The focus is on analyzing natural treatment systems for wastewater (NTSW) within the context of Macaronesia, considering factors such as life cycle assessment (LCA), carbon footprint, impacts, and mitigation capacity. The analysis of real case data from the Canary Islands and Cape Verde will inform the development of appropriate technologies tailored to different areas and scales within Macaronesia. This work includes a comprehensive life cycle analysis of the Santa Catarina (Cape Verde) NTSW. This analysis encompasses: a) Inventory analysis of the construction phase: This involves the assessment of inputs and outputs associated with the construction of the NTSW, including materials, energy consumption, transportation, and waste generation. The maintenance and operation phase is then evaluated, with a focus on the ongoing maintenance and operation activities required for the NTSW, including energy consumption, water usage, chemical inputs (if any), labour, and equipment maintenance. Finally, the impacts of the NTSW are evaluated. The environmental, social, and economic impacts generated by the NTSW are assessed. This includes an analysis of factors such as carbon emissions, water usage, land use, ecosystem impacts, human health effects, and economic costs. By conducting a comprehensive analysis of the Santa Catarina NTSW, the document aims to provide insights into the environmental performance and sustainability of the system. This information can then be used to identify areas for improvement, develop mitigation strategies, and inform decision-making processes regarding wastewater treatment technologies in Macaronesia. Furthermore, lessons learned from real case studies in the Canary Islands and Cape Verde can be applied to similar regions within the Macaronesia archipelago.

Advanced Finite Element (FE) modeling and simulations were performed on vehicular crashes into a Three-bar Metal Bridge Rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH Evaluation Criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52-m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%.

Bermejo Algebras, which consist of Algebra B and Treon Algebra, are non-associative and unital algebraic structures that introduce new complex entities. Recently, a Hausdorff space associated with the space where these algebras act was defined. We constructed a second-countable treon space utilizing a novel metric derived from these algebras. We defined topological spaces and established a metric to prove the second-countable property within Bermejo Algebras. Our findings contribute to the construction of manifolds in treon spaces by defining a countable base using the density of rationals in the real numbers.

Understanding whether the long-term follow-up (LTFU) system is working for families is critical to measuring the success of newborn screening (NBS) and understanding why some families are lost to follow-up. Caregivers were recruited from six pediatric specialty care clinics. Data was gathered from caregivers via six focus groups (n=24) and open-ended responses on a survey (n=33). Caregiver participants represented a wide range of NBS-related conditions and children’s ages. While this is not the first study to gather caregivers’ input, it is unique in its breadth of perspectives (e.g., metabolic, endocrine, hemoglobinopathy, etc.) and its focus on LTFU. When asked about goals for their children, caregivers identified health-related goals (i.e., ability for children to care for themselves, not being hindered by diagnosis) and non-health related goals (i.e., defining themselves outside of disease, participating in sports, making friends). In qualifying the LTFU care they want and need for their child and the key factors that influence access and engagement, caregivers identified three themes: communication and relationships with providers; care team roles and factors; and care access and utilization factors. The themes identified are not disjoint; they are intertwined and collectively illustrate the lived experiences of families at the epicenter of LTFU care.

Background: Surgical parameters of phacoemulsification can have a significant impact on behavior of anterior hyaloid membrane (AHM). Methods: In a prospective study using anterior segment optical coherence tomograph the attachment or detachment of AHM of 82 eyes were examined after uneventful phacoemulsification preoperatively and postoperatively over a 1-year period. The impact of capsulorhexis size, number of hydrodissections, difficulty of nucleus rotation, nuclear sclerosis grade, cumulative dissipated energy (CDE), ultrasonic time, total surgical time, difficulty of irrigation/aspiration, intensity of capsular polish, weakness of zonular fibers, presence of lens materials in Berger's space (LM-BS), and fluid usage were investigated related to the behavior of AHM. Results: In presence of zonular weakness and high CDE, a significant linear trend was observed regarding anterior vitreous detachment (AVD) at all postoperative times. Similarly, AVD is observed at 1-month, 3-month, and 1-year follow-up in cases of high nuclear sclerosis grade and high fluid usage. A significant linear trend was observed in the case of zonular weakness, hard nucleus and extensive capsular polish with LM-BS, while CDE, total surgical time, and fluid usage were significantly higher in these cases. Conclusions: Zonular weakness, high CDE, hard nucleus and high fluid usage are risk factors of postoperative AVD.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

Background: Gender dimorphism and marital status on colorectal cancer mortality have been previously documented, but the relationship between these factors and DNA mismatch repair protein (MMRP) expression status is unknown. Methods: Colectomy specimens were reviewed retrospectively for patients between 2018 to 2023 with demographics including race/ethnicity, gender, marital status, faith, body mass index, pathologic staging, and MMRP expression status. Statistical analyses were performed by using baseline characteristics tables and various programs in R-package. Results: A total 1018 colectomies were reviewed, and the tumor stages were significantly higher in right colon (stage 3 and 4) than those in left colon and rectosigmoid colon (p

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions has never been more urgent. This paper explores the crucial role of data automation (DA) and decision support systems (DSS) in enhancing decarbonation efforts within the realms of solid waste management (SWM), wastewater treatment (WWT), and contaminated soil remediation (CSR). Specifically, the paper provides: (i) an overview of the carbon footprint (CFP) in relation to environmental management (EM) and the role of DA and DSS in decarbonization; (ii) DA case studies in areas of SWM, WWT, and CSR; (iii) 3. life cycle assessment (LCA) based DSS case studies in areas of SWM, WWT, and CSR; (iv) multi-criteria decision analysis (MCDA) based DSS case studies in areas of SWM, WWT, and CSR; and (v) optimal contractual delivery methods based DSS case studies in EM practices. The analysis disclosed that adoption of DA and DSS in SWM, WWT, and CSR holds significant potential for decarbonizing these EM processes. By optimizing operations, enhancing resource efficiency, and integrating renewable energy sources, these technologies contribute to the reduction of GHG emissions and promote sustainable environmental practices. As the demand for more effective and eco-friendly solutions grows, the role of DA and DSS will become increasingly pivotal in achieving global decarbonization goals.

Standardised forms for capturing clinical data promote consistency in data collection and analysis across study sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1,886 hospitals and 950,064 patients were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs & symptoms (68.9% and 78.4%) and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results indicate that clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

Transition metal dichalcogenides (TMDs) monolayers exhibit unique valleytronics properties due to the dependency of the coupled valley and spin state at the hexagonal corner of the first Brillouin zone. Precisely controlling valley spin-polarization via manipulating the electron population enables its application in valley-based memory or quantum technologies. This study uncovered the uncompensated spins of the antiferromagnetic oxide (NiO) serving as the ferromagnetic (FM) order to induce valley spin-polarization in molybdenum disulfide (MoS₂) monolayers via the magnetic proximity effect (MPE). A spin-resolved photoluminescence spectroscopy (SR-PL) was employed to observe MoS₂, where the spin-polarized trions appear to be responsible for the MPE, leading to a valley magnetism. Results indicate that local FM order from the uncompensated surface of NiO could successfully induce significant valley spin-polarization in MoS₂ with the depolarization temperature approximately at 100 K, which is relatively high compared to related literature. This study reveals new perspectives and potential of AFM materials in the field of exchange-coupled van der Waals heterostructures.

Background. Myocardial involvement mediated by chronic obstructive pulmonary disease (COPD) is a common cause of morbidity and mortality. Conventional transthoracic echocardiography (TTE) parameters are poor in the detection of subclinical myocardial dysfunction. Aim.To investigate the contribution of strain in the early detection of cardiac damage in stable COPD patients Methods. Group A and B patients with COPD were enrolled in this study. The COPD assessment test, spirometry, 6-minute walk test, and both conventional TTE and strain were performed in these patients. Results. 80 COPD patients, with a mean age 65.6 ± 8.9 years, were included. The. Left ventricular ejection fraction (LVEF) was 60.7 ± 5.1%. Right atrium and right ventricle (RV) strain were 24.5 ± 6.6% and -19.9 ± 3.7%, respectively. Additionally, left ventricle global longitudinal strain (LV GLS) was -21.1 ± 2. Forty-eight patients had impaired RV strain. Compared to COPD patient with normal RV strain, those with RV reduced starin had a lower 6meter walk distance (6MWD) (p=0.001) and forced expiratory volume in the first second (FEV1) (p=0.012), and a higher CAT score (p=0.012). A reduced RV strain was correlated with a higher risk of hospitalizations for acute exacerbation in the post inclusion year (55% versus 25%; p=0.024). No deaths were recorded during the follow-up period. No significant factors causing neither RA starin alteration nor LV GLS reduction were revealed. Conclusion. Group A and B COPD patients having normal conventional TTE parameters, speckle tracking is a key parameter in the detection of subclinical myocardial dysfunction.

Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenge of viral infections and their growing resistance to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised Multi-Query Similarity Search Models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models' robustness and reliability. The top-performing model, M13+, demonstrated impressive results with an accuracy of 0.969 and a Matthew’s correlation coeffi-cient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine learning and deep learning AVP predictors. The MQSSMs outper-formed these predictors, highlighting their efficiency in terms of resource demand and public ac-cessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date.

The gut microbiota constitutes a complex ecosystem, comprising trillions of microbes that have co-evolved with their host over hundreds of millions of years. Over the past decade, a growing body of knowledge has underscored the intricate connections among diet, gut microbiota, and human health. Bioactive polysaccharides (BPs) from natural sources like medicinal plants, sea-weeds, and fungi have diverse biological functions, including antioxidant, immunoregulatory, and metabolic activities. Their effects are closely tied to the gut microbiota, which metabolizes BPs into health-influencing compounds. Understanding how BPs and gut microbiota interact is critical for harnessing their potential health benefits. This review provides an overview of the human gut microbiota, focusing on its role in metabolic diseases like obesity, type II diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and cardiovascular diseases (CVD). It explores the basic characteristics of several BPs and their impact on gut microbiota. Given their significance for human health, we summarize the biological functions of these BPs, particularly in terms of im-munoregulatory activities, blood sugar, and hypolipidemic effect, providing a valuable reference for understanding the potential benefits of natural BPs in treating metabolic diseases. These properties make BPs promising agents for preventing and treating metabolic diseases. The comprehensive understanding of the mechanisms by which BPs exert their effects through gut microbiota opens new avenues for developing targeted therapies to improve metabolic health.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots are becoming part of human society, they gradually assume the role of partners for humans wherever necessary: as assistants, collaborators, or companions. Companion robots are sup-posed to provide social contact to those humans who could not have it otherwise. These com-panion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored ter-ritory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting acceptance of robots as intimate partners, public perception of robots in in-timate roles, and the possible influence of cross-cultural differences in these domains. We also discuss possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term “Intimate companion robots” to reduce the negative connotations of other terms used so far and improve the social perception of research in the domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

Wicked problems, such as climate change, systemic poverty, and healthcare inequalities, rep- resent the most pressing and intractable challenges facing society today. We propose a novel approach that leverages complex systems theory as a comprehensive framework for understanding and tackling these challenges. By applying concepts such as state space analysis, attractors, and nonlinear dynamics, we offer a sophisticated toolkit for mapping the intricate web of interactions defining wicked problems and exploring potential interventions. Our approach moves beyond tradi- tional reductionist methods, embracing the inherent complexity and dynamism of these issues. We demonstrate how this framework can lead to more nuanced, adaptive, and effective strategies for addressing persistent societal problems. Despite the prevalence of wicked problems and the avail- ability of complex systems theory, practical applications of this approach remain uncommon. This paper aims to bridge this gap, illustrating the potential of complex systems theory to transform our engagement with intractable problems and pave the way for more robust, holistic solutions. By synthesizing insights from mathematics, physics, and social sciences, we present a transdisci- plinary approach that promises to revolutionize how we conceptualize and address society’s most challenging issues.

This study provides a thorough examination of the introduction of 5G technology in Bangladesh's agriculture sector, providing insights into the problems and potential implications related to its adoption. Focusing on the contextual landscape of Bangladesh, the study evaluates the effectiveness, efficiency, scalability, and ease of implementation of 5G technology concerning key challenges faced by the agricultural sector. Drawing comparisons with global perspectives, including countries such as China, Taiwan, Colombia, Netherlands, and India, the analysis provides a nuanced understanding of the diverse technological landscapes. The examination encompasses the evolution from 4G to 5G, emphasizing the commitment of major telecommunications providers in Bangladesh to advance technological capabilities. The challenges in rural adoption, limited technology literacy among farmers, and high smartphone costs are discussed, highlighting potential barriers to seamless integration. To address the creation of a more comprehensive technological infrastructure, the study also explores the efforts and regulatory support provided by the government and the Bangladesh Telecommunication Regulatory Commission (BTRC). Furthermore, the comparative analysis sheds light on the unique characteristics and advancements in other countries, contributing to a holistic understanding of 5G's role in transforming global agricultural practices. By exploring the anticipated socio-economic factors influencing the pace and success of 5G adoption in the agricultural sector, this paper emphasizes the need for collaborative efforts, infrastructure development, and addressing accessibility challenges to achieve widespread agricultural technology integration.

Increasing population size and income growth are causing and increasing demand for food and protein of animal origin. Insects are an interesting alternative to conventional animal source proteins as they can convert by-products from the agricultural industry to biomass for commercial feed for livestock and potentially as a food source for human consumption. Microorganisms have been found to affect insects and can be accumulated via horizontal and vertical transmission. This study aimed to identify if the removal of egg- and substrate-associated microorganisms impact larval performance through the following parameters: development of biomass, final biomass, and the survival rate of house fly (Musca domestica) larvae. Four treatments were tested on substrate consisting of 10.81% alfalfa (Medicago sativa), 21.62% wheat bran (Triticum aestivum), and 67.57% water: (A) disinfected eggs and non-autoclaved substrate, (B) non-disinfected eggs and autoclaved substrate, (C) disinfected eggs and autoclaved substrate, and (D) a control without any removal of microbiota. The results showed a significant decrease in final biomass for the treatments with only disinfected eggs, only autoclaved substrate and both when comparing to the control, and a significant decrease in survival rate for B and D. Moreover, the development of biomass showed a significant difference between days within all treatments. Together this suggest that microorganisms of housefly eggs and the growth substrate plays an important role for growth, which is critical in commercial insect production. Further studies must be performed to examine these parameters in more commercially relevant substrates.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study, conducted from 1 March 2023 to 31 March 2024 at sub-district health-promoting hospitals in Thailand, aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of profes-sional nurses in maintaining service standards under new governance. Utilizing a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes, we collected data from 340 nurses and 400 patients through structured questionnaires. These questionnaires were divided into seven sec-tions, capturing metrics such as the personal data of nurses, hospital service activities, and lead-ership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality and conducted paired t-tests to compare performance metrics before and after the admin-istrative transfer. One-way ANOVA was used to assess variability among different service units, and binary logistic regression helped identify key determinants of service quality. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited mini-mal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities, potentially contributing to enhanced healthcare delivery across restructured health systems.

This study investigates the characteristics of activated carbons derived from biomass plant residues: rice husk (OSL) of the genus Oryza, reed stalks (PhAC) of the genus Phragmites, pine sawdust (PSL) of the genus Pinus, and wheat straw (TAL) of the genus Triticum. Activation was carried out using potassium hydroxide, employing a two-stage pyrolysis under air. The study examined the influence of activation temperature, duration, concentration of the impregnating agent, and impregnation time. Characteristics such as sorbent porosity, bulk density, and hydrocarbon sorption capacity were determined. Adsorption capacity was assessed using BET analysis and the iodine number determination method, with the highest iodine number achieved being 2809 mg/g, indicating high adsorption capability. The structural morphology of the activated carbons was evaluated by SEM, and their chemical surface characteristics were identified by IR spectroscopy. Computational analysis of the nitrogen sorption isotherms using the Barrett-Joyner-Halenda (B-J-H) method and the Dubinin-Radushkevich (D-R) equation showed that activated carbon samples from rice husk and pine sawdust possess a developed specific surface area and contain micro- and mesopores. The Dubinin-Astakhov (D-A) approach was used to study the pore diameter of activated carbon, revealing significant findings on pore structure. The maximum micropore volume was found in activated carbon samples from pine sawdust (PSL), measuring 0.46 cm³/g. All activated carbon samples of plant origin were tested under field conditions for the remediation of oil-contaminated areas near the “Zhanatalap” oil field in the Atyrau region. By the 16th day, the oil-contaminated soil treated with rice husk activated carbon was cleaned by 67.1%.

Building on swampy land is a major challenge due to the soft and waterlogged soil properties. Traditional approaches using wooden pile foundations have long been used but new technology with a more environmentally friendly and efficient approach needs to be applied to swampy land.The objectives of this research are: to obtain a conceptual model of modified wooden pile foundation construction combined with concrete, to identify the impact of soil condition changes due to foundation implementation and the swampy groundwater environment, and to determine the application of the 'constructed wetland' and rain garden adaptation methods to the foundation model concept.In this research, a qualitative method was used with a descriptive approach based on literature studies and data collection through surveys/field observations. There is interdisciplinary collaboration from architectural, material, and environmental fields. The method conducted includes experimental testing of the foundation model concept. The research stages are: identifying variations in foundation design on wetland, survey/field study observation of foundations in Banjarmasin, architectural-material-environmental analysis of the foundation model concept with rain garden adaptation, and finally, conclusions and suggestions.The rain garden concept is an innovation that can help address waterlogging issues by enhancing the improvement of foundation construction and drainage in swampy soils. In this paper, the use of galam wood, wooden pile foundations, and construction technology innovations to improve swampy soil conditions are discussed in detail.

This study delves into the impact of artificial intelligence on students’ learning.The paper pioneers the use of kmeans classification to reduce the dimensionality of features before the classification process, which minimizes redundant information and computational overhead. This step is not commonly found in prior research.Firstly, a random forest model is established with “whether AI assistance was used to complete the paper” as the output, and various features as inputs. The analysis of the predictive accuracy when changing input features reveals the degree of influence of each feature on using AI to complete the paper. After feature selection, the remaining 6 features can still effectively predict the use of AI to complete the paper, indicating the representativeness of the extracted indicators. Subsequently, the PCA-K-means algorithm is employed to classify the use of artificial intelligence. PCA algorithm is used for dimensionality reduction of the extracted features to better characterize students’ use of AI. Three representative variables are selected, and the contribution of each feature is computed based on the factor loading matrix. Finally, the K-means algorithm is used to classify students based on the three dimensional features, showing that students can be effectively classified into 2 categories. The results indicate that the majority of students support the use of AI technology to enhance their abilities, although some students may only intend to use AI to cope with exams and similar tasks.

While many studies have described the association between cognitive decline and eating habits, little attention has been paid to its association with cheese intake. In this cross-sectional study of 1,035 community-dwelling women aged ≥ 65, we investigated the association between intake/type of cheese and cognitive function. The anthropometry, functional ability, and the frequency of food intake, including cheese, were assessed. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and a score of 20–26 was defined as mild cognitive decline (MCD). We found that MMSE score was significantly different between the presence of cheese intake (cheese intake: 28.3±1.9; non-cheese intake: 27.8±2.2) and between the type of cheese consumed (Camembert cheese: 28.7±1.3; others: 28.3±1.9). After adjusting for confounders, multiple logistic regression revealed four significant independent variables for MCD: Camembert cheese intake (odds ratio [OR] = 0.448, 95% confidence interval [CI] = 0.214–0.936), age (OR = 1.114, 95% CI = 1.059–1.171), usual walking speed (OR = 0.2620, 95% CI = 0.109–0.621), and repetitive saliva swallowing test scores (OR = 0.865, 95% CI = 0.750–0.995). Our results, while based on cross-sectional data from Japanese community-dwelling older women, demonstrated that Camembert cheese intake is significantly associated with MCD.

The Zero Trust Architecture (ZTA) security system follows the "never trust, always verify" principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation is time-consuming and difficult. Azure's extensive services and customizations require careful design and implementation. Azure administrators struggle to navigate and change configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTA's comprehensive policy definitions, multi-factor and pass-wordless authentication, and other advanced features are tested in a mid-size business scenario. These configuration changes require a solid grasp of Azure's architecture and Zero Trust. Implementing an Azure ZTA reduces vulnerabilities and restricts authorized users' access to critical resources. Implementation is time-consuming and expensive, requires a significant commitment to changing IT procedures, and can be confusing because the same features are available in multiple places. Azure ZTA has shown great potential for both hybrid and cloud-native companies.