Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impact its operational lifetime. Reducing the energy loss of HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Predictive models for the opening response time, HSV driving energy, and Joule energy using Back Propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for HSV driving strategy holds great significance in improving its working durability.

The meaning of a good social outcome in land use transport policy/planning has been relatively underdeveloped, compared to economic and environmental sustainability goals, but this is now changing. Cities are increasingly prioritizing reducing social exclusion, with the allied intent of providing all residents with equitable access to the benefits of their city. However, in jurisdictions that use cost-benefit analysis to guide government decision-making, this poses a challenge: monetization of the benefits of reducing social exclusion is poorly developed. An evaluation gap thus confronts benefit-cost based assessment of initiatives intended to reduce social exclusion, compared to initiatives directed towards societal economic and environmental goals. This is a particular problem for public transport services that primarily enable people at risk of mobility-related social exclusion to access more opportunities in their society (social transit). The benefits of reducing social exclusion associated with these services have not been monetized, so social transit initiatives are poorly placed to argue their merits against competing initiatives where benefit monetization is more advanced (e.g., mass public transport and roads). The paper summarizes Australian research that has developed monetized measures for the value of increased trip making, bridging/bonding social capital, sense of community and several conceptions of wellbeing, and of reducing neighbourhood disadvantage, as these contribute to reducing social exclusion. New Singaporean research on the value of trip making to support reduced exclusion is also presented, affirming the Australian trip values. Trip values are then used in case study examples to show they can make a material difference to the societal worth of initiatives intended to reduce exclusion. Using these trip values, and other pathway values linked to reducing social exclusion, closes or at least substantially reduces the evaluation gap confronting social goal achievement in land use transport policy and planning, strengthening the case for reducing exclusion. These values will assist jurisdictions that are seeking to provide their residents with more equitable access to the benefits of their city.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

This paper proposes a radial alternating materials phononic crystal (RAM-PnC). By simulating the band gap structure of the phononic crystal, it is verified that there is a complete acoustic band gap at the resonant frequency of 175.14 MHz, which can prevent the propagation of elastic waves in a specific direction. The proposed alternately arranged radial phononic crystal structure is applied to the thin-film piezoelectric-on-silicon (TPOPS) MEMS resonator. The finite element simulation method increases the anchor quality factor (Qanchor) from 60596 to 659536011 at the operating frequency of 175.14 MHz, which is about 10,000 times higher. The motion resistance of the RAM-PnC resonator is reduced from 156.25 Ω to 48.31 Ω compared with the traditional resonator. At the same time, the insertion loss of the RAM-PnC resonator is reduced by 1.1 dB compared with the traditional resonator.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

The purpose of this study was to determine the income, costs and revenue of the Gontor Putri 3 bakery. Want to know the efficiency of the bakery business in Karangbanyu Regency. The method used in this research is analytical descriptive by conducting a survey. The data used in this research is primary data. Data collection techniques use observation, interviews and documentation, while the process of analysis is quantitative descriptive. Using the R/C ratio, a value of 1.72 per month is obtained. This value indicates that the bakery factory in Karangbanyu is efficient because the value is more than one.

Respiratory viral infections (RVI) are common reasons for healthcare consultations. The inpatient management of RVI consumes significant resources. From 2009-2014, we assessed the costs of RVI management in 4776 hospitalized chil-dren aged 0-18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by de-tailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU (“non-ICU”) versus management requiring ICU-care (“ICU”) added up to 2,767.14€ (non-ICU) vs. 29,941.71€ (ICU) for Influenza, 2,713.14€ (non-ICU) vs. 16,951.06€ (ICU) for RSV infections, and 2,767.33€ (non-ICU) vs. 14,394.02€ (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all 8 RVI studied: Influenza, RSV, hRV, ade-novirus (hAdV), metapneumovirus (hMPV), parainfluenzavirus (hPIV), bocavirus (hBoV) and seasonal coronavirus (hCoV) infections. ICU-costs for Influenza however, ex-ceeded all other RVI. At the time of the study, Influenza was the only RVI with antivi-ral treatment options available for children, but only 9.8% of Influenza patients (non-ICU) and 1.5% of ICU-patients with Influenza received antivirals; only 2.9% were vac-cinated. Future studies should investigate the economic impact of treatment and pre-vention of Influenza-, COVID-19-, and RSV post vaccine introduction.

Ecological changes evoke many felt losses and types of grief. These affect sustainability efforts in profound ways. Scholarship on the topic is growing, but the relationship between general grief research and ecological grief has received surprisingly little attention. This interdisciplinary article applies theories of grief, loss, and bereavement to ecological grief. Special attention is given to research about “non-death loss” and other broad frameworks about grief. Dynamics related to both local and global ecological grief are discussed. Kinds of potential losses arising out of ecological issues are clarified with the frameworks of tangible/intangible loss, ambiguous loss, nonfinite loss, and shattered assumptions. Various possible types of ecological grief are illuminated by discussing the frameworks of chronic sorrow and anticipatory grief/mourning. Earlier scholarship about disenfranchised ecological grief is augmented by further distinctions about various forms it may take. The difficulties in defining complicated or prolonged grief in ecological context are discussed, and four types of “complicated ecological grief” are explored. Based on the findings, three special forms of ecological loss and grief are named and discussed: transitional loss and grief, lifeworld loss, and shattered dreams. The implications of the results for ecological grief scholarship, counselling and coping are briefly discussed. The results can be used by psychological and healthcare professionals and researchers, but also by members of the public who wish to reflect on their eco-emotions.

The advent of zirconia ceramics with excellent mechanical, biomechanical and optical properties has made it an attractive metal-free substitute for titanium implants. Both animal and human studies have documented shortcomings with titanium implants. A review of the current literature on 3 iterations of zirconia implant designs has been challenging due to heterogeneous success data and limited follow-up. Zirconia implants hold promise for a new generation of dental implants, but technical developments are needed for design and material enhancements that will need to be validated by long-term rigorous studies.

In the field of computational fluid dynamics (CFD) for rotorcraft, two significant challenges are resolving the complex vortex structures in rotor wakes, and representing the moving rotor blades in the ambient airflow. This study addresses the first challenge by using a third-order unstructured finite volume solver, which, compared to its second-order counterpart, has substantially lower dissipation. Consequently, even relatively coarse meshes are capable of resolving small vortices. With this background flow field solver, the second challenge is addressed by modeling each rotor as an Actuator Disk Model (ADM), or describing individual rotor blades as actuator lines, designated as the Actuator Line Model (ALM). Both of these models are equipped with an improved correction for aerodynamic losses at blade tips, which is thoroughly presented in the methodology section. The numerical experiments section centers on analyzing errors linked to various sampling approaches. Additionally, the article discusses comparisons between vortex theory and ALM, specifically regarding calculations for fixed-wing aircraft. Furthermore, high-order precision and parallel efficiency are exemplified in scenarios encompassing rotors engaged in both hovering and forward flight rotors. The results in this paper demonstrate that the combination of the ALM/ADM with the improved tip loss correction and the third-order finite volume solver presents a new way of developing efficient tools for the aerodynamic analysis of helicopter rotors.

Due to an urgent need for reducing the fast progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of heat demand coverage of buildings at district and city level must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of heat demand, city planners, energy agencies seek a tool, which will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path-Analysis (LCPA) and Closest Facility (CF) allow to find the shortest and “cheapest” way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA and CF based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only few open-source data input, like street networks and building footprints are needed, the methods can be implemented in all communities of the country of Poland. In this paper one example of one community of Wroclaw county, called Siechnice and surroundings is presented.

Background: Retinal diseases such as neovascular age related macular degeneration (nAMD), diabetic macular edema (DME) or branch-/central retinal vein occlusion (B/CRVO) have significant implications for patients' social and psychological well-being. To gain a comprehensive understanding of patients' backgrounds and attitudes, we developed an exploratory questionnaire, the Basic Care and Patient Satisfaction Questionnaire (BPZ-9). Methods: The ALBATROS study aimed to assess the care situation of treatment-naïve patients diagnosed with either nAMD, DME or B/CRVO. The data collection took place at the beginning of treatment and after twelve months of anti-VEGF (vascular endothelial growth factor) therapy. The BPZ-9 instrument comprises nine questions to evaluate patients’ psychological and social situation and their satisfaction with treatment. Results: Data were collected from 1.478 nAMD (mean 78 years), 445 DME (67 years), 233 BRVO (70 years) and 144 CRVO (71 years) patients at 102 study centers throughout Germany. Approximately one third of the patients was living alone and six percent were in organized care. One in four patients had difficulties to walk and one in five needed accompany for treatment. Anxiety to lose vision was present in three out of four patients at the beginning and slightly decreased to two out of three over the 12-month treatment preriod. The distress of having a retinal disease was generally higher than the distress related to treatment. Most patients reported high treatment satisfaction (73%) and felt well informed (81%). Conclusion: Patients receiving anti-VEGF therapy commonly experience impaired mobility, dependency on others, distress and the fear of vision loss.

Drowning remains a prominent global pediatric health concern, necessitating preventive measures such as educational initiatives for children and caregivers. This study aimed to assess the feasibility and educational effectiveness of an interactive puppet show centered on water safety for children and parents. A 30-minute original theater performance, featuring two actors and three puppets (a girl, a crab, and a lifeguard), was conducted. Subsequently, 185 children (aged 4 to 8) and their 160 parents (134 mothers and 26 fathers) participated in this quasi-experimental study. Pre- and post-show tests were administered to evaluate knowledge and behaviors in aquatic environments. Prior to the puppet show, 78% of children exhibited basic aquatic competency. Only 33% considered swimming alone risky. Following the intervention, 81.6% of children changed their perception of the risks of solo beach activities, showing improved knowledge of emergency number activation (from 63.2% to 98.9%, p<0.001). The intervention increased parents' intention to visit lifeguard-patrolled beaches and improved their CPR knowledge for drowning victims by 58.8%. In conclusion, a drowning prevention puppet show positively impacts children and parents, potentially enhancing safety behaviors during water-related leisure activities, warranting consideration as part of comprehensive drowning prevention strategies.

Borderline ovarian tumors (BOTs) show intriguing characteristics distinguishing them from other ovarian tumors. This unique type of non-invasive neoplasms is characterized by atypical growth of epithelial cells, nuclear atypia, and a moderate-level mitotic activity that places BOTs between benign tumors and invasive cancers. Similar to invasive carcinomas, BOTs can be categorized into six histological subtypes based on the type of epithelial cells present. The most prevalent subtypes are serous and mucinous BOTs, whereas endometrioid, clear cell, seromucinous, and borderline Brenner tumors are diagnosed less often. Noticeably, molecular changes found in BOTs can vary on a case-by-case basis, which warrants further research on the molecular landscape of these tumors. Identifying carcinogenic mutations through molecular analysis and developing targeted therapies represent significant advancements in the diagnosis and treatment of ovarian malignancies. A growing body of evidence indicates that BOTs often remain clinically dormant for extended periods before a molecular trigger initiates increased cell replication, potentially leading to carcinoma development or recurrence. Continued research in this area is crucial for improving risk assessment and developing personalized treatment approaches. While molecular studies have contributed significantly to our understanding of BOT pathogenesis, substantial research is still required to elucidate the relationship between ovarian neoplasms and extraneous disease, identify accurate prognostic indicators, and develop targeted therapeutic approaches. The aim of the present systematic review was to analyze the spectrum of molecular changes found in BOTs and discuss their significance in the context of the overall therapeutic approach.

Resistance evolution during exposure to non-lethal levels of antibiotics is influenced by various stress responses of bacteria which are known to affect growth rate. Here, we aim to disentangle how the interplay between resistance development and associated fitness costs is affected by stress responses. We performed de novo resistance evolution of wild-type strains and single-gene knockout strains in stress response pathways using four different antibiotics. Throughout resistance development, the increase in minimum inhibitory concentration (MIC) is accompanied by a gradual decrease in growth rate, most pronounced with amoxicillin or kanamycin. By measuring biomass yield on glucose and whole-genome sequences at intermediate and final timepoints, we identified two patterns of how the stress responses affect the correlation between MIC and growth rate. First, single-gene knockout E. coli strains associated with reactive oxygen species (ROS) acquire resistance faster and mutations related to antibiotic permeability and pumping out occur earlier. This increases the metabolic burden of resistant bacteria. Second, the ΔrelA knockout strain which has reduced (p)ppGpp synthesis, is restricted in its stringent response, leading to diminished growth rates. The ROS-related mutagenesis and the stringent response are vulnerabilities within the fitness of resistant strains that can possibly be targeted to prevent development of resistance.

Osteoporosis, often linked to increased osteoclast activity due to menopause or aging, was the focus. In this study, the inhibitory effects of Salicornia europaea L. (SE) water extract of desalted SE (WSE) were investigated on osteoclast differentiation and bone loss in ovariectomized mice. The findings revealed that WSE effectively inhibited RANKL-induced osteoclast differentiation, as evidenced by TRAP staining, and also suppressed bone resorption and F-actin ring formation in a dose-dependent manner. Gene expressions related to osteoclast differentiation, including NFATc1, TRAP, cathepsin K, and DC-STAMP, were downregulated by WSE. Oral administration of WSE improved bone density and structural parameters in ovariectomized mice. Dicaffeoyl quinic acids (DCQAs) and saponins in WSE were detected, with 3,4-, 3,5-, and 4,5-DCQA isolated and identified. All tested DCQAs, including the aforementioned types, inhibited osteoclast differentiation, bone resorption, and the expression of osteoclast-related genes. Furthermore, WSE and DCQAs reduced ROS production mediated by RANKL. These results indicate the potential of WSE and its components, DCQA, as preventive or therapeutic agents against osteoporosis and related conditions.

The decline in assisted reproductive technology (ART) success among older women, attributed to decreased oocyte quantity and quality, poses a significant challenge. Currently, no consensus ex-ists on the optimal ovarian stimulation protocol for older women undergoing IVF. This retrospec-tive registered cohort study aimed to compare cumulative live birth rates (CLBR), time to live birth (TTLB), and cost-effectiveness in women over 35 years using gonadotropin-releasing hor-mone agonist (GnRHa) or clomiphene-primed ovarian stimulation (CPOS). We performed pro-pensity score matching (PSM) on 2,871 IVF cycles in women over 35 who received either GnRHa or CPOS protocols, resulting in 375 cycles in each group to compare treatment outcomes. Addition-ally, a decision tree model was utilized to assess the cost-effectiveness of the two protocols. Fol-lowing PSM, both groups showed similar baseline characteristics. The CPOS protocol exhibited a higher cycle cancellation rate (13.07% vs 8.00%, p=0.032), yet maintained comparable fertilization and embryo quality. While CLBRs per initial cycle (41.07% vs. 45.33%, p=0.269) and delivery out-comes were similar between the groups after a 24-month follow-up, TTLB was longer in the CPOS group. Notably, the average cost per live birth in the CPOS group was 21.27% less than that in the GnRHa group (￥32,301.42 vs ￥39,174.22). In conclusion, for women over 35 undergoing IVF, the CPOS protocol demonstrated similar CLBR to GnRHa but with lower costs, suggesting its potential as a viable and cost-efficient ovarian stimulation option.

Grain supply is the lifeblood of a country, and the stability of the supply chain is a crucial prerequisite for ensuring national grain security. This paper draws on the definition of resilience in physics and economics. It takes supply chain fracture resilience, impact resilience, and synergy resilience as the secondary indicators. It constructs a comprehensive evaluation indicator system of the grain supply chain resilience, measures the resilience indicator of China's grain supply chain from 1996 to 2021, and analyzes the role of supply, cost, exchange rate, and other external shocks in influencing the resilience of China's grain supply chain on this basis. The study found that the overall level of China's total grain supply chain resilience has been growing year by year and can be divided into three stages: low-level stabilization stage, continuous growth stage, and high-level stabilization stage. Grain supply chain fracture resilience has been growing steadily, grain supply chain impact resilience fluctuation is more obvious, and grain supply chain synergy resilience has been changing more gently. In the inquiry of the impact of external shocks on the resilience of China's grain supply chain, it was found that world grain exports and the RMB exchange rate have a significant positive impact on China's grain supply chain resilience level, while the international oil price has a significant negative impact. Based on this, the paper puts forward suggestions for ensuring stable production and supply in the grain market, improving the structure of foreign trade in grain, and actively coping with international commodity price shocks.

Estimating age at death is essential to establish a biological profile from human skeletal remains in both forensic and archeological settings. Imaging studies of skeletal age changes in adults have described metamorphosis of trabecular bone structure and bone loss in the proximal femur , as well as changes in morphology during different stages of life. This study aims to assess the utility of a digital representation of conventional X-ray films of the proximal femur for the estimation of age at death in a sample of 91 adult individuals (47 females and 44 males) of the Coimbra Identified Skeletal Collection. The proposed approach showed a root mean squared error (RMSE) of 17.32 years (and mean absolute error of 13.47 years) for females and an RMSE of 14.06 years (mean absolute error of 11.08 years) for males. The main advantage of this approach is the consistency in the feature detection and extraction, as X-ray images projected on the femora space will always produce the same set features to be analyzed for age estimation while more traditional methods rely heavily on operator experience which can lead to inconsistent age estimates among experts.

The paper presents the main experimental capabilities of the automatic system for the study of the interaction between porous media and atmosphere. This system is one of the possible variants of patent 2-241-477. The paper shows the result of a drying and wetting study of a tailings sample. The sample is 32 cm in height and 29 cm in diameter. The saturation and drying process was carried out at 26 degrees Celsius at the sample surface, an air velocity of 2 m/s and a relative humidity of 60%. The recharge applied to the column surface was 0.3 cm3/cm2/minute. The results show the effectiveness of the system in controlling the spatial-temporal variation of different physical properties of the porous medium in the vertical and surface profile. The controlled variables include volumetric water content, suction, relative humidity, sample weight variation due to recharge or drying, shrinkage, temperature, etc. The experiment shows how the tailings saturate very quickly but dry very slowly. Tailings consolidation occurs in the drying and wetting processes, being higher in the first one.

This work presents a new algorithm to resolve the problem of distribution network reconfiguration using the Tabu Search metaheuristic and voltage drop. The proposed method presents a different way to obtain the initial solution using the voltage drop, as well as a new way to generate a neighborhood of candidate solutions, based on randomness and the voltage drop, to perform a smooth and steady descent execution of the algorithm. The proposed algorithm has the main objective to reduce the active power losses in distribution networks, but it also achieves an refinement in the voltage profile. The proposed method was tested in two distribution systems, the 33-Bus and 94-Bus systems, commonly found and tested in the literature. The results of the tests show that the proposed method presents a low computational cost, achieves good solutions, and is better than the other methods found in the literature.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

The efficiencies of conventional triple organic Rankine cycle (TORC) configurations are restricted by the low critical temperatures of common working fluids (＜320 °C). This paper proposes a high-temperature TORC system. A near-azeotropic mixture biphenyl/diphenyl oxide (BDO), which has stellar track record in the high-temperature ORC applications, is innovatively adopted as the top and middle ORC fluid simultaneously. The top cycle evaporation temperature is up to 400 °C in view of BDO’s high stability. Five conventional organic fluids are chosen for the bottom ORC. Fundamentals of the TORC system are illustrated, and mathematical models are built. Thermo-economic performance is investigated. The results hint that the optimal thermodynamic property is achieved on the use of benzene as the bottom fluid. The maximum thermal and exergy efficiencies are respectively 40.86% and 74.14%. The largest irreversible loss occurs inside the heat exchanger coupling the middle and bottom ORCs, accounting for above 30% of the total entropy generation. The levelized energy cost (LEC) is 0.0284 $/kWh. Given the same heat source condition, the TORC system can boost efficiency by 1.02% and drive down LEC by 0.0024 $/kWh compared with a BDO mixture-based cascade ORC.

Conventional greenhouse accounting inadequately describes land use/land use change and forestry (LULUCF) emissions, cooling emissions, and sequestration potential. As we enter the age of drawdown, we propose an accounting framework that offers greater consistency and transparency. By unfolding net accounting of LULUCF CO2 emissions; aggregating biosphere sinks; accounting for all emissions (heating and cooling); com-paring sectors with emissions-based Effective Radiative Forcing (ERF) rather than global warming potential; and including drawdown potential of land use carbon opportunity cost (COC), this reveals fresh insight into sector contributions and mitigation potential. Consistent gross emissions reporting of LULUCF CO2 emissions finds these to be at least 3.5 times greater than conventionally understood. Consolidating natural sequestration on ‘managed’ and ‘intact’ land, we find that since 1750 vegetation and the oceans have removed from the atmosphere an amount equivalent to 2.4 times cumulative fossil fuel CO2 emissions, demonstrating the immense drawdown potential of the biosphere. This accounting places deforestation (responsible for 77% of LULUCF emissions) as the main source of historic CO2 emissions, and attributes drawdown potential COC to sectors. The most extensive land use sector, animal agriculture, was found to have contributed the greatest warming (52% of net ERF since 1750) and to offer the greatest drawdown potential COC.

There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. We developed a Machine Learning (ML)-driven prediction algorithm to trigger and explain a communication alert for BT based on anticipated peri-operative events irrespective of BT policies. We analysed prospectively collected data from 403 EOC patients who underwent cytoreductive surgery between 2014 and 2019. We calculated the estimated blood volume (EBV) using the formula EBV = weight x 80 and set off 10%EBV as threshold for individual intervention. Based on the known estimated blood loss (EBL) we identified two groups. We employed Receiver operating characteristic (ROC) curves for performance metrics. The model performance for the above threshold prediction was satisfactory (AUC 0.823, 95% CI 0.76-0.88). The top feature commonly shared between interrogators was operative time (OT). Intra-operative blood loss of at least 10%EBV was associated with OT>250 minutes, primary surgery, serous histology, performance status 0, R2 resection and surgical complexity score >4. Large bowel resection, stoma formation, ileocecal resection/right hemicolectomy, mesenteric resection, bladder and upper abdominal peritonectomy were amongst sub-procedures clearly associated with increased intervention risk. Precise prediction of blood requirements is not possible unless a rough estimate of OT is known in advance.

Preserving and restoring biodiversity is becoming a great challenge as we face a world where planetary boundaries will likely be crossed over the following decades. Such challenge needs to consider multiple scales of complexity, both in space and time. A common thread in most cases is the presence of nonlinear phenomena generating shifts among alternative states. These breaking points imply a new perception of risk and different management strategies. A broad range of phenomena affect the preservation of healthy communities and constrain the ways to deal with conservation, from local features associated with habitat loss or facilitation to mesoscale or global network-level ecological complexity and the role played by extreme events. How are these scales connected? How can the emergent properties associated with ecosystem dynamics be exploited? Here a synthesis of ideas is presented, with a complex systems view of the different scales involved, the emergent phenomena separating them, and the universal properties that allow defining simple models on each scale.

Background: Autoimmune inner ear disease (AIED) is a rare autoimmune condition characterized by progressive sensorineural hearing loss with hearing fluctuations over weeks to months, presenting significant challenges in both diagnosis and treatment. Due to its rarity, treatment responses and prognosis in AIED remain poorly understood. Methods: This study reviewed medical records from Taipei Medical University Hospital documented between August 2016 and August 2021. Among 67 patients experiencing fluctuating hearing loss, 11 met the diagnostic criteria for AIED and underwent regular follow-up assessments within a 5-year timeframe. ROC curve was used to discriminate the power of these prognostic parameters. Results: In this study, several factors including younger age, milder initial hearing loss, meeting criteria for bilateral Meniere's disease (MD), having an ascending audiogram pattern, longer time interval between hearing impairment attacks in contralateral ears and responding well to steroids were associated with better prognosis. ROC curve demonstrated the diagnostic value of specific parameters such as the presence of bilateral MD (AUC 0.825, 95% CI 0.640–1.000), pretreatment HI ≤ 45 dB HL (AUC 0.808, 95% CI 0.610–1.000), response to steroids (AUC 0.800, 95% CI 0.595–1.000), and ascending audiogram (AUC 0.858, 95% CI 0.683–0.858). Conclusions: This study identified key prognostic factors for AIED, providing valuable insights for predicting AIED prognosis and facilitating personalized patient management.

1960s Europe saw a large number of residential neighborhoods built to house those migrating from the countryside. Today, more than 50 years later, these neighborhoods suffer high levels of functional, social, and technical obsolescence. In response to this, University of Seville developed the Aura Strategy as an intervention methodology to find global solutions to issues in outdated neighborhoods. To provide visibility to this aspect of the Aura strategy, the retrofit proposal presented in this article provides a solution to improve the roofing of buildings in a case study neighborhood (Polígono de San Pablo, Seville) and an analysis of the results in terms of energy and financial savings for local residents. The results show that for a population of roughly 18,000 (in 2018), net savings, including energy and retrofitting costs, ranging from nearly 6.5 to over 8.6 million can be made over the 20-year life cycle span. Likewise, the results obtained on the reduction of thermal loads indicate a 72% decrease in energy consumption, equivalent to a saving of close to 4,500 tons of greenhouse gas emissions for the district and the entire life cycle time period, with the consequent benefits on the impact on air quality and and the fight against climate change. climate change.

Abstract: A two-year study of pearl millet-groundnut cropping systems across two fertilizer levels was conducted at the INRAN/TARNA research station in Maradi, Niger in the Sahelian zone of West Africa. The objective of this study was to identify the best cropping system with and without fertilizer application to optimize pearl millet and groundnut yields, land use efficiency, and economics as measured by value-to-cost ratio. The experiment was conducted with three replications, and ten treatments combination of five pearl millet and groundnut cropping systems (pearl millet and groundnut sole crops, and three pearl millet – groundnut intercrops), and two fertilizer levels. Sole cropping produced the highest pearl millet grain by over 300 kg ha-1 and groundnut pod yields by over 500 kg ha-1, but all the intercropping systems had a LER above 1.0 with land use efficiency increased by 19 to 41%, also intercropping increased diversity, and reduced risk. Fertilizer application increased yields in all cropping systems by 200 to 600 kg ha-1. Government subsidy increased the value-to-cost ratio by 0.5 to 2, 5 units, and was required for economic response for pearl millet sole and intercrops in 2022. The groundnut sole cropping system had the greatest economic response to fertilizer application. Based on the risky environment and multiple end uses needed by producers, the intercrop system M-G: 1:3:1 with fertilizer application is usually the best option to optimize pearl millet and groundnut production.

Derivatives products have become essential in portfolio diversification, price discovery and risk hedging. Derivatives are complex instruments; their role is twofold, risk management and speculation, and their actual Impact on the underlying assets’ behaviours are not well understood. Little is documented empirically on how these instruments’ two-edged roles influence firms’ financing decisions, firm risk exposures, and stability. Given the growing interest in using derivatives in risk management and portfolio engineering, this study examines the practical impact of derivatives usage on the underlying firm’s financing policy and stability. The paper uses data from South African listed non-financial firms for the period 2000 to 2019. The study employs a dynamic panel model estimated with System Generalised Methods of Moments (GMM). The initial analysis shows that derivatives use reduces the cost of capital and increases firm stability. However, further in-depth analysis provides evidence that extensive use of derivatives increases firms’ capital costs and negatively impacts financial stability. These findings imply that the risk embedded in derivatives’ speculation dominates their risk management function. The results are subjected to numerous controls for robustness, including financial leverage, firm size, cashflows and asset tangibility.

As a new logistics system, Physical Internet (PI) provides a solution to cope with the continuous rise of urban logistics demand by exploring same-level and cross-level paths and flattening. This research suggests a PI-based urban logistics distribution cost minimization model based on the examination of the primary elements influencing PI distribution, intending to maintain the logistic transportation process' integrity. This proposed model enhances the flexibility of goods operation and reduces the global cost of logistics distribution using the interconnected and open PI logistics distribution mode. The experimental results show that the distribution cost of the PI logistics model is significantly better than that of a traditional logistics model. The elastic and cost advantages relatively grow as the infrastructure for distribution scales up and the interruptions from emergencies occur.

The aim of this study was to evaluate the clinical and radiographic outcomes of the All-on-4 technique in fully edentulous maxilla using R2GATE software for flapless, computer-guided surgery and immediate loading. Ten adult patients with fully edentulous maxilla, aged between 35 and 60 years, were included in the study between April 2021 and April 2022. The surgical procedure was performed under local anesthesia and followed the All-on-4 approach with immediate loading of implants using a screw-retained provisional acrylic resin prosthesis on the same day. Final prosthesis was delivered four months after the surgery. Clinical and radiographic outcomes were evaluated at four- and twelve-months post-operation. The study reported a 95% implant survival rate using the All-on-4 technique. The mean marginal bone level was recorded at 0.35 mm and 0.66 mm during the 4 and 12-month follow-up periods, respectively. Fixed provisional prosthesis fracture, abutment, and prosthesis screw loosening were the most frequent mechanical complications reported. Within the limitations of this study, the use of computer-guided surgery in the All-on-4 technique seemed to be a promising treatment option with high implant survival rates that may help reduce post-surgical discomfort and mechanical complications in the rehabilitation of the edentulous maxilla.

In the FCC conversion of heavy petroleum fractions as atmospheric residues, the main challenge for refiners to achieve the quantity and quality of various commercial products depends essentially on the catalyst used in the process. A deep characterization of the catalyst at different steps of the process (fresh, regenerated, and spent catalyst) was investigated to study the catalyst's behavior including the physicochemical evolution, the deactivation factor, and kinetic-thermodynamic parameters. All samples have been characterized by various spectroscopy methods such as N2 adsorption-desorption, UV-visible spectroscopy, Raman spectroscopy, LECO carbon analysis, Scanning electron microscopy (SEM), X-ray diffraction (XRD), X‐ray fluorescence (XRF), nuclear magnetic resonance spectroscopy (NMR13C) analysis, and Thermogravimetric analysis. The results of N2 adsorption-desorption, UV-vis, Raman, LECO carbon, and SEM images showed that the main causes of catalyst deactivation and coking were the deposition of carbon species that covered the active sites and clogged the pores, and the attrition factor due to thermal conditions and poisonous metals. The XRD and XRF results showed the catalyst’s physicochemical evolution during the process and the different interlinks between catalyst and feedstock (Nickel, Vanadium, Sulfur, and Iron) elements which should be responsible for the coking and catalyst attrition factor. It has been found that, in addition to the temperature, the residence time of the catalyst in the process also has an influence on catalyst structure transformation. NMR13C analysis revealed that polyaromatic hydrocarbon is the main component in the deposited coke of the spent catalyst. Thermogravimetric analysis showed that the order of catalyst mass loss was fresh > regenerated > spent catalyst due to the progressive losses of the hydroxyl bonds (OH) and the structure change along the catalyst thermal treatment. Moreover, the kinetic and thermodynamic parameters showed that all zones are non-spontaneous endothermic reactions.

Soiling accumulated on a photovoltaic (PV) module can significantly reduce the transmittance of the cover glass, resulting in power losses and consequent economic losses. Natural atmospheric parameters influence the accumulation of soiling at the various geographic locations. In this paper, the approaches and outcomes of the research studies on either indoor (simulator-based) or outdoor (field-based) PV soiling have been thoroughly reviewed. Different parameters depicted for the power loss of PV modules are analyzed individually and presented. Moreover, this study delves into a detailed examination of the key factors influencing dust depositions on PV modules in various geographical regions, with a particular focus on their relationship with climatic conditions. This way, probable future research directions to quantify soiling losses are identified. In addition, different loss prevention and mitigation techniques are also reviewed. This makes it possible to highlight effective strategies and pinpoint potential future research lines in these areas.

This study tests the utilization of Bluetooth noise-canceling headphones in improving the quality of eye exams in patients with hearing loss. University of Texas Medical Branch patients above 55 years of age were screened for hearing loss using SoundCheck audiometry. All 29 subjects (18 red-zone and 11 yellow-zone) answered pre-recorded Amsler grid and patient education questions that solicited precise responses. As controls, subjects were randomly administered half of the questions via headphones and half via a smartphone at normal speech volume (60 decibels). Points were awarded for responses demonstrating comprehension, and a post-exam survey was collected. Collectively, the mean score was 1.79 with headphones versus 0.96 with control on the Amsler grid segment and 1.90 with headphones versus 0.97 with control on education questions (p=0.001). Between the red and yellow zone hearing loss patients, the red-zone group answered significantly better in both Amsler (1.78 versus 0.50; p=0.0003) and education questions (1.88 versus 0.44; p<0.0001) with headphones. The yellow zone group answered better with headphones overall but failed to reach significance. Post-exam survey indicated that 28 of 29 patients (97%) preferred the headphones during ophthalmic exams. Patients with severe hearing loss demonstrated better comprehension with the noise-canceling headphones; thus these devices show great promise in improving effective, compassionate communication between providers and hearing-loss patients.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degrada-tion, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at Standard Test Condition (STC) efficiency. After considering losses, the system generates 2815 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27744 BDT from selling energy to the grid and operating costs of 64060.60 BDT/year. Remarkably, this initiative can prevent 33074.061 tCO2 emissions over the project’s 25-year lifespan.

Antimicrobial resistance (AMR) poses a global public health threat, with rates directly linked with consumption. The World Health Organization (WHO) AWaRе-classification aims to guide antibiotic use, which is influenced by drug availability, affordability, and economic policies. In Jordan, a high proportion of consumed antibiotics belong to the 'Watch' category. Data from the WHO-AWaRе classification, the Essential Medicines List, and the Jordan Food and Drug Administration were analyzed. Antibiotics for adults were classified and their cost per Defined Daily Dose (DDD) was determined, then affordability was assessed. In 2023, 43 injectable and 43 oral antibiotics for adults were registered in Jordan. 'Watch' antibiotics were the most common. 'Access' antibiotics had the lowest cost/DDD. 'Rеsеrvе' antibiotics were the most expensive with few generics. Injectable antibiotics had a negative correlation between cost and the number of alternatives. Affordability was higher for oral antibiotics compared to injectable ones. 'Rеsеrvе' antibiotics were generally unaffordable. This study highlights the need to promote 'Access' antibiotics over other categories by encouraging registration of missing ‘Access’ antibiotics and adjusting 'Watch' and 'Rеsеrvе' antibiotic prices. Competition among generics can lead to lower prices, increasing affordability and accessibility. We emphasize the importance of the AWaRе-classification in guiding antibiotic use in Jordan.

Liquidity providers for asset-pair pools with constant value distri- bution, such as Balancer pools, experience some impermanent loss whenever there is price divergence in the tokens’ fiat values. In this paper, we model this impermanent loss for geometric mean invariant pools by deriving a function of two parameters. We analyze our function graphically, showing that unevenly distributed pools with large weighting disparities best mitigate the risk of impermanent loss. We conclude by providing a general protocol for liquidity providers to choose a suitable pool based on their risk tolerance and profit goals.

Acute respiratory viruses (ARVs) are leading cause of diseases in human worldwide. High risk individuals including children and elderly could potentially develop severe illness that could result in hospitalization or death in the worst case. Most common ARVs are Human respiratory syncytial virus, Human Metapneumovirus, Human Parainfluenza Virus, rhinovirus, coronaviruses (including SARS and MERS CoV), adenoviruses, Human Bocavirus, enterovirus (-D68 and 71), and influenza viruses. The olfactory deficits due to ARVs infection is a common symptom among patients. This mini review provides an overview of the role of SARS-CoV-2 and other common ARVs in the development of human olfactory pathophysiology. We highlight the critical need for understanding the signaling underlying the olfactory dysfunction and the development of therapeutics for this wide-ranging category of AVRs to restore the altered or loss of smell in affected patients.

The electrical motor design requires a lot of work and resources. In the field, motor designers need to decide on competitive motor sizing quickly to compete with other motor suppliers. Generally, it requires expensive FEM S/W to get accurate motor sizing results, but buying and maintaining many licenses is costly. Moreover, it needs some patience to get the result from FEM analysis. In this paper, the motor sizing technique using an existing production motor is presented to respond to urgent requests from customers without using FEM analysis. In order to enhance the fidelity of the result, magnetic flux loss by magnet coating and non-linear characteristics are considered during the motor sizing process. The proposed sizing method was verified by comparison to the FEM analysis and test result and showed reasonable confidence in them. Also, this method has the substantial advantage of outputting accurate results instantly without taking any time. It helps with the modification design of electrical motors without using expensive FEM S/W and a certain level of skill or experience.

A friction loss model for brake testing platform is constructed to revise the braking force test results, which can largely improve the accuracy of automotive brake performance testing. Specifically, the influence of tire pressure, vehicle axle load, placement angle and vehicle speed on the friction loss is tested and analyzed on a wheelbase adjustable automotive anti-force brake testing platform. The friction loss model for the platform is constructed based on the defined relative slip and placement angle, which is validated through actual tests on four types of vehicles. The results showed that the maximum relative error between the values of the model calculated simulation and the measured was 2.2%, indicating the friction loss model is valid and can provide a basis for revising the braking force of automotive brake. Meanwhile, the maximum braking force increases from 3982 N to 4772 N, increased by 19.8%, when the placement angle increased from 36.9° to 47.0°. Accordingly, in order to improve the accuracy of detection results of the automotive anti-force rolling brake testing platform, the friction loss caused by the change in the placement angle should be revised, and then braking rate of the vehicle should be judged according to GB 7258-2017 and GB 38900-2020.

This study investigates the role of insulation, in commercial buildings and how it affects energy efficiency and sustainability in different climate regions. Commercial buildings consume an amount of energy making them ideal candidates for energy saving measures. Insulation plays a role in maintaining indoor conditions reducing energy usage and minimizing carbon emissions by limiting heat transfer through the building’s envelope. By conducting simulations using the Hourly Analysis Program (HAP) this research examines how insulation thickness, climate conditions and building characteristics impact energy consumption and greenhouse gas emissions. The study covers climate zones with a focus on Afghanistan, semi-arid, arid and mountainous regions where sustainable construction practices are highly important. The findings demonstrate that insulation has an effect on enhancing energy efficiency particularly when it comes to heating. Additionally, economic factors, future research directions and broader implications, for construction practices are thoroughly discussed. Considering that the construction industry contributes significantly to carbon emissions this research provides insights into promoting sustainable building practices. By optimizing insulation strategies, architects, builders and policymakers can reduce the impact of commercial buildings while advancing energy efficiency and supporting a more sustainable urban future.

At present Coronavirus disease-2019 (COVID-19) is one of the leading contributing factor to mortality and the impact of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection on the sinonasal tract has become more prominent, particularly with the rising awareness of olfactory dysfunction (OD). We extracted data from published papers available in electronic databases (Wiley online library, PubMed, and Nature). We used the following search terms alone or with combinations - Olfactory dysfunction, SARS-CoV-2, mechanism and treatments. We found worldwide up to 98% of patients confirmed OD due to COVID-19. Current studies have implied that regardless of the high self-reported recovery rate, 25–40% of patients after 1 or 2 months and approximately 15%–28% of patients at six months struggle to fully restore their sense of smell. Moreover, female sex, younger and older age, active smoking, and chronic lung disease are reported as the associated risk factors of OD. Although the pathophysiological mechanism of action(s) of the OD is yet to be explored in depth, central nervous system (CNS) entrance, olfactory bulb (OB) and sustentacular cell damage, neural routes inflammation, non-neuronal cells damage, decreased OB volume and deregulation of olfactory receptor genes are among the commonly reported mechanisms for the development of OD.

Optic neuritis (ON) is the most common cause of vision loss in young adults. It manifests as acute or subacute vision loss, often accompanied by retrobulbar discomfort or pain on eye movements. Typical ON is associated with Multiple Sclerosis and is generally mild and steroid-responsive, whereas atypical forms are characterized by unusual features, such as optic disc edema, poor treatment response and bilateral involvement, and are often associated with autoantibodies against aquaporin-4 (AQP4) and Myelin Oligodendrocyte Glycoprotein (MOG). However, in rare cases AQP4 and MOG antibodies will come back negative, plunging the clinician in a diagnostic conundrum. Seronegative ON warrants a broad differential diagnosis, including autoantibody-associated, granulomatous, and systemic disorders. These rare forms need to be identified promptly, as their management and prognosis is greatly different. The scope of this review is to describe possible, rarer etiologies of ON when AQP4 and MOG antibodies are negative and discuss their diagnosis and treatment.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. Controlling and curing diabetes in its early stages is the most significant technique to avoid its effects of diabetes. However, lack of awareness and expensive clinical tests are the primary reasons to skip clinical diagnosis and take preventive methods in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, the study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes. Because clinical features are not always known to all people from lower-income countries. To find the best fit supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. The study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for growing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a computer data capture and visualization platform foundation for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design theory based on extensive data analysis. Additionally, the system presents information on occupants' behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human-machine communication and enabling timely adjustments to design strategies for new buildings and operation and maintenance strategies for existing buildings.

Polycystic ovary syndrome (PCOS) is an insulin resistant state, compensated by hyperinsulinemia. More than 50% of the women with PCOS are obese, and/or have metabolic syndrome. Weight loss improves both metabolic and reproductive outcomes. Not only the energy content, but also the nutrient composition of a diet may be important. This article will present a series of studies from our research comparing effects of dietary protein vs. simple-carbohydrates (CHO). Results of the acute challenge studies demonstrate that simple-CHO intake causes reactive hypoglycemia in one third of the women with PCOS, especially among obese and insulin resistant individuals. Symptoms of hypoglycemia are associated with secretion of cortisol and adrenal androgens. Simple-CHO suppresses the hunger signal ghrelin for a shorter period. During weight loss, women who receive protein supplementation achieve larger amounts of weight- and fat-mass losses. Amino acid compositions of the protein supplements do not affect the improvements in weight and insulin resistance. It is plausible that simple-CHO intake leads to weight gain, or interferes with weight-loss, by causing reactive hypoglycemia, triggering adrenal steroid secretion, and leading to snacking. Since obese women with PCOS are more susceptible to reactive hypoglycemia, a vicious cycle establishes. Restriction of simple-CHO may break this cycle.

Thailand has formulated her climate change policy and updated relevant plans and policies to align with the goal of achieving carbon neutrality and net zero GHG emissions. This study investigates the optimal level of GHG mitigation in Thailand, taking into account the marginal abatement cost (MAC) and social cost of carbon (SCC). It evaluates how energy efficiency and renewable energy technologies influence GHG reduction in the power and industrial sector and illustrates policy recommendations that align with the 2020 to 2050 policy and plan period. The findings indicate that there may be instances where GHG mitigation potential is insufficient to reach the national mile-stone. In such cases, it becomes imperative to leverage all technologies within the marginal abatement cost curve (MACC) and also utilization of the social cost of carbon for policy decision making and meeting the desired goals. In certain scenarios, the adoption of additional technologies or measures may be necessary, such as flexible power generation and deploying carbon capture and storage or hydrogen which are high-cost technologies. Furthermore, preparations should be made for multiple levels of climate change policies and plans beyond 2030.

Aims: Multidisciplinary diabetic foot clinics have been shown to be an effective therapeutic model for reducing major amputations and mortality rates. The aim of this article is to assess the economic impact of the implementation of a multidisciplinary diabetic foot clinic in a hospital in Barcelona, Spain. Methods: Observational cost-benefit study of all subjects with diabetes admitted with the diagnosis of a major amputation due to diabetic foot from 2010 to 2020. Direct and indirect costs were compared before and after the introduction of the multidisciplinary diabetic foot clinic. Results: The individual cost per patient with diabetic foot was €14,768 before the implementation of the unit. After the implementation of the clinic, the expected cost was reduced to €5,985 due to a 40% reduction in the probability of hospitalization. Overall, the implementation of the clinic resulted in cost savings per patient valued at €8,783, of which, €7,165 are related to hospital benefits. Conclusion: The results of this analysis contributes to the evidence suggesting that multidisciplinary diabetic foot clinics are cost-effective, by demonstrating that they have a positive impact on patient health while also reducing the utilization of healthcare resources.

The research paper provides a mathematical framework for understanding phase shift in wave phenomena, bridging theoretical foundations with real-world applications. It emphasizes the importance of phase shift in physics and engineering, particularly in fields like telecommunications and acoustics. Key equations are introduced to explain phase angle, time delay, frequency, and wavelength relationships. The study also introduces the concept of time distortion due to a 1° phase shift, crucial for precise time measurements in precision instruments. The research also addresses infinitesimal wave energy loss related to phase shift, enriching our understanding of wave behavior, and impacting scientific and engineering disciplines.

This article presents a new design of supporting tethers through the concept of force distribution. The transmitted force applied on tethers will be distributed on the new tether design area resulting in low acoustic energy transferred to anchor boundaries and stored energy enhancement. This technique achieves an anchor quality factor of 175,000 compared to 58,000 obtained from con-ventional tether design representing 3-fold enhancement. Also, the unloaded quality factor of the proposed design improved from 23,750 to 27,442 representing 1.2-fold improvements.

Background: this study aimed to describe the distribution of the genotype and allele frequencies of GJB2 variants in the Chinese population of the Dongfeng Tongji cohort and to analyze the features of the hearing phenotype. Methods: we used data from 9910 participants in the Dongfeng Tongji cohort in 2013 and selected nine GJB2 variants. Pure-tone audiometry was employed to measure hearing. Differences in genotype and allele frequencies were analyzed by chi-squared test or Fisher's exact test. Results: of the 9910 participants, 5742 had hearing loss. The genotype frequency of the GJB2 variant p.V37I was statistically significantly distributed between the normal and impaired hearing groups, but not for the variant c.235delC. A higher frequency of the p.V37I homozygous genotype was found in the hearing loss group (0.5%) than in the normal hearing group (0.1%). Patients with p.V37I and c.235delC homozygous mutations exhibited varying degrees of hearing loss, mainly presenting sloping and flat audiogram shapes. Conclusions: a significant difference was found in the genotype frequency of the GJB2 variant p.V37I between the case and control groups, but not for the variant c.235delC. Different degrees of hearing loss and various audiogram shapes were observed in patients with p.V37I and c.235delC homozygous mutations.

Temporal lobe epilepsy is a common, chronic disorder with spontaneous seizures that is often refractory to drug therapy. A potential cause of temporal lobe epilepsy is primary brain injury, making prevention of epileptogenesis after the initial event an optimal method of treatment. Despite this, no preventive therapy for epilepsy is currently available. The purpose of this study was to evaluate the effects of anakinra, lamotrigine, and their combination on epileptogenesis using the rat lithium-pilocarpine model of temporal lobe epilepsy. The study showed that the treated and untreated animals showed no significant difference in the number and duration of seizures. However, the severity of seizures was significantly reduced after treatment. Anakinra and lamotrigine, alone or in combination, significantly reduced neuronal loss in the CA1 hippocampus compared to the control group. However, the drugs administered alone were found to be more effective for CA3 than their combination. The treatment alleviated the impairments in activity level, exploratory behavior and anxiety, but had a relatively weak effect on TLE-induced impairments in social behavior and memory. The efficacy of the combination treatment did not differ from that of anakinra and lamotrigine monotherapy. These findings suggest that anakinra and lamotrigine, alone or in combination, may have clinical utility in preventing epileptogenesis.

This research examines the fire resistance of geopolymer composites (GCs), formulated using alka-li-activated metakaolin and aluminum powder. The production method involves spray-coating carbon steel plates with fresh GCs, followed by a 28-day curing period at room temperature. Sub-sequently, fire performance and aging tests are conducted to evaluate fire resistance. The study investigates GC properties, including apparent density, bending strength, compression strength, thermal conductivity, porosity, and production cost. Morphological changes before and after fire and aging tests are analyzed using scanning electron microscopy (SEM). Results demonstrate GC foams' outstanding thermal insulation, providing up to 75 minutes of fire resistance with a 6 mm coating, reducing temperatures by 300 °C compared to uncoated steel. GCs exhibit a density of 670 kg/m³, thermal conductivity of 0.166 W/m·K, 68% porosity, and cost efficiency at 250 dollars per cubic meter. This study underscores the potential of foamed geopolymer coatings for effective and economical surface protection.

Eosinophilic Granulomatosis with Polyangiitis (EGPA) is a rare systemic necrotizing vasculitis, affecting small-to-medium sized vessels. EGPA’s clinical manifestations are heterogeneous affecting different organs and systems and the upper respiratory tract can be affected with Ear, Nose and Throat (ENT) involvement. The aim of our study was to assess type manifestations at time of diagnosis in a cohort of EGPA patients and correlate findings with baseline variables (sex, age, antineutrophil cytoplasmic antibodies –ANCA- status) and literature reports. Main ENT manifestations in our patients at time of diagnosis were: nasal polyposis (CRSwNP) (52%), turbinate hypertrophy (48%), nasal swelling (40%), rhinorrhoea (40%), chronic rhinosinusitis without nasal polyposis (CRSsNP) (32%), nasal bone deformities (32%), nasal crusts (20%), nasal mucosal ulcers (12%), corditis (12%), hoarseness/dysphonia (12%), hearing loss (12%), mucoceles(4%) eosinophilic rhinitis (4%). No correlations were found between sex, age, ANCA status and ENT clinical manifestations. A polymorphic ENT involvement is often observed at early stages of EGPA. The presence of nasal, sinus, ear and/or laryngeal manifestations in patients with asthma and hypereosinophilia, independently of sex, age or ANCA status, should raise allert for further investigation and differential diagnosis for EGPA. ENT specialists should be aware of their leading position in this diagnostic race.

Aims: The objective of this study was to assess the cost-effectiveness of the CardioMEMS HF system in a HF Clinic in Spain by evaluating the real-time remote monitoring of pulmonary artery pressures, which has shown to reduce heart failure (HF) related hospitalizations and improve the quality of life for selected HF patients. Particularly, the study aimed to determine the value of CardioMEMS in Southern Europe, where healthcare costs are significantly lower and its effectiveness remains uncertain. Methods: This single-centre study enrolled all consecutive HF patients who had been implanted with a pulmonary artery pressure sensor (CardioMEMS-HF system, Abbott Laboratories, Abbott Park, IL, USA). The number of HF hospitalizations in the year before and the year after the sensor implantation was compared and quality-adjusted life years (QALY) gained based on a literature review of previous studies was calculated. Results: The rate of HF hospitalisations was significantly lower at 1 year compared with the year before CardioMEMS implantation (0.25 versus 1.10 events/patient-years, HR 0.22, p=0.001). At the end of first year, the usual management outperformed the CardioMEMS HF system, resulting in a net monetary value difference of 2,540€ per patient and a benefit-cost ratio of 0.38. However, by the end of the second year, the CardioMEMS system is estimated to reduce costs compared to usual management. Conclusions: Based on the results, we suggest that remote monitoring of pulmonary artery pressure with the CardioMEMS HF system represents a long-term cost-effective strategy in a healthcare setting in Southern Europe.

The purpose of this study was to investigate the functional load capacity of the periodontal ligament (PDL) in a full arch maxilla and mandible model using numerical simulation. The goal of was to determine the functional load pattern in multi- and single rooted teeth with full and reduced periodontal support. CBCT data were used to create 3D-models of a maxilla and mandible. The DICOM dataset was used to create a CAD-model. For a precise description of the surfaces of each structure (enamel, dentin, cementum, pulp, PDL, gingiva, bone), each tooth was segmented separately, and the biomechanical characteristics were considered. A Finite-Element-Analysis (FEA) software computed the biomechanical behavior of stepwise increased force of 700N in cranial and 350N in ventral direction of the muscle approach of the Masseter muscle. The periodontal attachment (cementum-PDL-bone contact) was subsequently reduced in 1mm increments and the simulation repeated. Quantitative (pressure, tension, and deformation) and qualitative (color-coded images) data were recorded and descriptively analyzed. The teeth with the highest load capacities were the upper and lower molars (0.4-0.6MPa), followed by the premolars (0.4-0.5MPa) and canines (0.3-0.4MPa) when vertically loaded. Qualitative data showed that the area with the highest stress in the PDL were for single rooted teeth the cervical and apical area and for molars additionally the furcation roof. In both, single- and multi rooted teeth the gradual reduction of the bone levels caused an increase of the load on the remaining PDL. Cervical and apical areas as well as the furcation roof are the zones with the highest functional stress. The more bone loss, the higher the mechanical load on the residual periodontal supporting structures.

Our findings indicate that all five phytocannabinoids reduce HG-HL-induced -cell loss likely through reducing apoptosis and pyroptosis. The protective effects of CBD, THCV, CBC, and CBN were seen in the GSIS impairment by HG-HL. Although all five phytocannabinoids tested in this research demonstrated the capability to inhibit β-cell dedifferentiation induced by HG-HL, CBD seems to be more effective compared to the other phytocannabinoids, as indicated by the specific biomarker responses of β-cells and progenitor cells to CBD.

Robotic surgery is a rapidly developing and evolving surgical technique. This literature review evaluates the advantages and disadvantages in the context of gallbladder disease comparing robotic Cholecystectomy to the well-established laparoscopic Cholecystectomy. Significant advantages are reduced operative blood loss, hospital stays and post operative pain, alongside increased patient satisfaction and improved visualization and precision compared to the laparoscopic approach. Disadvantages include lack of tactile feedback, significant cost attributed to equipment, training requirements and increased procedural time due to the set-up sequences. The implementation of robotic surgery also poses significant changes for the training of future surgeons. Whilst it can standardize training and increases opportunity for simulation, the concern is the loss of traditional skills. Currently the evidence base remains mixed however the use of robotics in the right setting, with suitably trained operatives and appropriate patients can enable improved outcomes for patients with gallbladder disease. There is a lot of scope for development including the implementation of tactile feedback to aid operative technique and artificial intelligence to aid patient safety. Likely broadening the use and potential benefit of robotic surgery not only for cholecystectomy but many surgical specialties.

Results of profile loss measurements, including trailing edge flow details, are presented for the flow of an organic vapor through a linear turbine cascade. The so-called VKI-I blade profile from the open literature was chosen for the cascade, and the working fluid was NOVEC 649. Pitot probes and hot wire anemometry were employed to measure the flow field up and downstream of the cascade. Details of the unsteady flow caused by the trailing edge of the blades and the turbulent spectrum were investigated using hot-wire anemometry. The new organic vapor flow results were compared with literature data obtained for air and with the prediction of conventional literature loss models. It was found that under certain thermodynamic conditions, specific traditional loss models can reasonably predict organic Rankine cycle (ORC) turbines' profile loss. Still, significant deviations between the loss models and the experimental data can also occur.

Pacing strategy refers to the distribution of effort and speed throughout the race to achieve optimal performance. The study aims to understand whether the choice of pacing strategy in swimming depends on the length of competitions and how sex, age, and performance level influence this strategy. Participants were the finalists of the 800-meter and 1500-meter freestyle events at the elite and junior world championships in 2022-2023. Race outcomes and pacing parameters were compared between the two distances and across different groups of swimmers. Swimmers in both distances did not break world records. Pacing strategy generally followed a U-shape with significant differences in the frequency and duration of speed changes between the two distances. The 800-meter exhibited more frequent changes in acceleration while the 1500-meter events generally followed a more consistent time series pattern. There were differences in pacing strategies between males and females and between junior and elite swimmers. Swimmers closer to world records showed more consistent pacing patterns compared to those farther from records. The study suggests that pacing strategies are influenced by race distance, sex, age, and performance level. The research highlights the complex interplay between physiological and psychological factors that shape a swimmer's decision-making during a race.

Escalation of commitment explains the choice for investing in an option for which great investments have previously been made even though better choices became available. The last 25 years of producing innumerous versions of Alzheimer Disease animal models with virtually no predictive validity should have exposed the fact that using animals for studying AD is clearly a failing course of action and as such should have been abandoned long ago, giving room to other alternatives to flourish. In this assay, I explore the idea that the investment in new animal models for studying Alzheimer (AD) represents a classic case of escalation of commitment, and sunk cost dilemma, in the biomedical research.

When training deep neural networks tasks, the most popular choices are cross-entropy loss. On the other hand, in general speaking, a decent loss function can take on shapes that are considerably more flexible and ought to be adapted for different activities and datasets. In most of the classification tasks, generally if the true class is not correctly recognized by the network (top1), that class is placed among the five classes with the highest probability (top5). This shows that the network does not necessarily recognize the correct class with a low probability, but a class similar to it (such as 3 vs. 8 in mnist) assigns a higher probability and this causes a mistake in that task. Accordingly, we proposed a loss function deals with the error of class that the neural network incorrectly recognized as correct, in addition to the correct class error. We call our proposed loss as False Positive Loss (FPL), with the intention of viewing and designing loss functions not only through the utilization of true class but also through the utilization of the value of false positive classes. One of the core properties of our proposed loss is full adaptability, which makes False Positive Loss be fully capable of getting reformulated by using other widely used loss functions formulas based on the task or the need of the users. Extensive experimental results demonstrate that our suggested loss function outperforms other well-known losses on a variety of tasks and datasets. As can be observed, the performance of our False Positive Loss is superior to that of the cross-entropy loss when it comes to tasks involving 2D picture classification. We have compared our loss with cross entropy as the most common classification loss function on some models (such as ResNet-18, ResNet-50 and Efficientnet-V2) through classification known as a basic computer vision task. with both random or pre-trained initial weights. As a result, in some cases the models with our loss outperform the same tasks with cross entropy from the viewpoint of some metric (i.e. accuracy and FP). For example, the resnet-50 on cifar-10 dataset with random initialization indicated a top1 accuracy of 94.93 with cross entropy and 95.25 with our loss, while for top5 accuracy the results are 99.86 and 99.87, respectively.

This research aims to develop an inexpensive ocean observation instrument with a project name is NOBEL (Nusantara Oceanography Backdoor Experiment Laboratory)-BOX. The device can be installed on all types of vessels and placed in a specific area to map the water conditions, providing accurate data for managing a marine area specifically to water quality. The principle of NOBEL-BOX is to attach sensors in a container connected to a microcontroller and then measure directly. The method employed includes experimental design, laboratory and field tests, and data evaluation to produce the necessary system and instruments. The design process involves the construction of the instrument and the fabrication of the device. This process includes creating three-dimensional sketches/drawings, designing microcontrollers and data transmission systems, and power capacity. The sensors attached to this device are surface water temperature, air temperature, salinity, dissolved oxygen, pH, and TDS. The testing phase includes data validation, testing of the device in the laboratory, and field testing. The laboratory and field tests showed that the device worked and provided data that can be used to analyze the seawater condition spatially and over time.

This study aimed to compare the differences in durability and prestress loss between normal-weight concrete (NC) and lightweight aggregate concrete (LWC) prestressed box girders, which were constructed at the same time in the same area, so as to verify the superiority of using synthetic lightweight aggregate (LWA) made from reservoir sediments in prestressed bridges. For the NCs and LWCs used in prestressed box girders, the basic mechanical properties were tested on the one hand, and the durability properties were tested on the other hand. Then, through the prestress monitoring system, the prestress loss of the two groups of the prestressed box girders was tracked. The results of the durability test confirmed that LWC can inhibit the penetration of air, water, and chloride ions by strengthening the interfacial transition zone between the aggregate and the cement paste, thereby improving its durability. Moreover, the magnetic flux prestress loss of the NC prestressed box girder reached 8.1%. In contrast, the magnetic flux prestress losses on both sides of the LWC prestressed box girder were 4.6% and 4.9%, respectively. This verified that, under the same environmental conditions, the use of LWC produced less of a prestress loss than the use of NC.

This publication aims to disseminate a step-by-step process that walks through the conceptualization and building of a low-cost (~ $150 monitoring device for airborne fine particulate matter (PM2.5), based on miniaturized sensors and components. Details on the implementation of the hardware and software are provided which facilitate the data acquisition, capture and analysis. The central components and their setup discussed in what follows include: the sensor device (called “P.ALP” – Ph.D. Air quality Low-cost Project), Arduino IDE (Integrated Development Environment) and R code (open-access software). A monitoring device for PM2.5, using low-cost sensors and technologies was successfully conceptualized, designed, and implemented. The P.ALP monitoring system was designed and developed to be a basic device, which can be further customized and implemented using the wide range of low-cost sensors available on the market.

The aim of this paper is to study the main areas in which Artificial Intelligence (AI) will impact the field of project management that relates to cost, risk and scheduling. The research model was based on a previous study of the ten project management knowledge areas presented in the PMI’s PMBOK 6th edition where project schedule-, cost- and risk management knowledge areas were identified as being the ones most likely to be affected by the development of AI. A group of experts that participated in the study agreed that AI will affect the project management profession in the future. Different elements of the three knowledge areas were considered to be affected more by AI than others. The schedule baseline is the element believed to be affected the most out of the project schedule management elements. For project cost management, the estimation of resource cost is believed to be affected the most. In the case of project risk management, the application of AI will have the strongest impact on the probability and impact format.

Tissue bioengineering has been assuming a pivotal role in implementing alternatives to conven-tional ossicular middle ear replacement prostheses to overcome extrusion while preserving acoustic properties. This in vitro study aims to explore for the first time in current literature the feasibility of a biohybrid middle ear prosthesis composed of titanium surrounded by bone ex-tracellular matrix as bio-coating. We have hereby studied the adhesion and proliferation of human adipose-derived mesenchymal stem cells (hASC) on titanium scaffolds in vitro. Moreover, we identified the osteogenic differentiation of hASC using immunofluorescence assay to analyze osteoblasts gene expression profile (Alp, Runx2, Col1a1, Osx, and Bglap) and we accounted the presence of collagen as a marker of hASC ability to secrete extracellular matrix. We utilized scanning electron microscopy to evaluate the presence of extracellular matrix on the scaffolds. Our preliminary data demonstrated the titanium ability to support human adipose-derived mesenchymal stem cell colonization, proliferation, and osteoblastic differentiation, to obtain a biohybrid device. Our experience seems encouraging, we advocate further in vivo research to corroborate our results towards bone transplantation.

Elliptic curve cryptography (ECC) over prime fields relies on scalar point multiplication realized by point addition and point doubling. Point addition and point doubling operations consist of many modular multiplications of large operands (256 bits for example), especially in projective and Jacobian coordinates which eliminate the modular inversion required in affine coordinates for every point addition or point doubling operation. Accelerating modular multiplication is therefore important for high-performance ECC. This paper presents the hardware implementations of modular multiplication algorithms, including 1) Interleaved modular multiplication (IMM), 2) Montgomery modular multiplication (MMM), 3) Shift-sub modular multiplication (SSMM), 4) SSMM with advance preparation (SSMMPRE), and 5) SSMM with CSAs and sign detection (SSMMCSA) algorithms, and evaluates their execution time (the number of clock cycles and clock frequency) and required hardware resources (ALMs and registers). Experimental results show that SSMM is 1.76 times faster than IMM, and SSMMCSA is 3.21 times faster than IMM. We also present the ECC hardware implementations based on the Secp256k1 protocol in affine, projective, and Jacobian coordinates using the IMM, SSMM, SSMMPRE, and SSMMCSA algorithms, and investigate their cost and performance. Our ECC implementations can be applied to the design of hardware security module systems.

The changes in storage loss, water distribution status, gel characteristics, TBARS, total volatile basic nitrogen, and total plate count of cooked reduced-phosphate silver carp surimi batter during cold storage at 4 °C were investigated. The storage loss, content of free water, pH, hardness, TBARS, total volatile basic nitrogen value and total plate count of all cooked reduced-phosphate silver carp surimi batters significantly increased (P < 0.05) with increasing the cold storage time. Meanwhile, the content of immobilized water, whiteness, springiness, and cohesiveness decreased significantly (P < 0.05). At the same cold storage time, the sample of cooked reduced-phosphate silver carp surimi batter has lower water mobility, darker colour and better texture characteristics, but the values of TBARS, total volatile basic nitrogen and total plate count were not significantly different (P > 0.05). To sum up, the use of potassium bicarbonate instead of sodium tripolyphosphate could produce the cooked reduced-phosphate silver carp surimi batter with better water holding capacity and gel characteristics during cold storage.

The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.

Microwave absorbers with lightweight, good stability and high efficiency have attracted much attention for their applications in many contemporary fields. In this work, the 3D porous (Ni@NO-C)n/NO-C composite absorber was prepared by a wet chemistry method with Ni chains and melamine as precursors, in which NO-C (N,O-doped carbon) encapsulated Ni particles are homogenously dispersed in the 3D porous networks of NO-C in the form of (Ni@NO-C)n chains. The special microstructure of the as-prepared material is proved to be benefit for the improvement of its microwave adsorption performance. The as-synthesized (Ni@NO-C)n/NO-C composite ab-sorber exhibited an effective absorption bandwidth of 4.1 GHz and an extremely large reflection loss of -72.3 dB. The excellent microwave absorbing performances can be ascribed to the coopera-tive consequence of dielectric loss and magnetic loss along with the balance between attenuation capability and impedance match.

Low-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2 (PLF) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM2.5), 10 μm or less (PM10), and nitrogen dioxide (NO2), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r<0.4). Moderate correlation was observed for one PLF unit at indoor location and two units at roadside location, when measuring PM2.5, but not for PM10 and NO2 concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R2 values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs.

The study of velocimetry is important for characterizing and comprehending the effects of fluid flow, and the Particle Image Velocimetry (PIV) technique is one of the primary approaches for understanding the velocity vector field in a test section. Commercial PIV systems are expensive, with one of the main cost factors being high-speed camera equipment capable of capturing images at high frames per second (fps), rendering them impractical for many applications. This study proposes an evaluation of utilizing smartphones as image acquisition systems for PIV technique application. An experimental setup inspired by the known angular displacement of synthetic particles is proposed. A stepper motor rotates a plate containing an image of synthetic particles on its surface. The motion of the plate is captured by the smartphone camera, and the images are processed using PIVlab-MatLab® software. The use of two smartphones is assessed, with acquisition rates of either 240 fps or 960 fps and varying angular velocities. The results were satisfactory for velocities up to 0.7 m/s at an acquisition rate of 240 fps and up to 1.8 m/s at 960 fps, validating the use of smartphones as a cost-effective alternative for the PIV technique.

Supermarkets in Port Harcourt (PH) city, Nigeria, predominantly rely on diesel electricity generation due to grid instability, leading to high cost of electricity prices. Although solar photovoltaic (PV) systems have been proposed as an alternative, these supermarkets have yet to adopt them, mainly due to high investment costs and a lack of awareness of the long-term financial and environmental benefits. This paper examines the technical and economic practicality of a PV system for these supermarkets using the PVsyst software and a spreadsheet model. Solar resources showed that PH has a daily average solar radiation and temperature of 4.21 kWh/m2/day and 25.73℃, respectively. Market Square, the supermarket with the highest peak power demand of 59.8 kW, and a 561 kWh/day load profile, was chosen as a case study. A proposed PV system with a power capacity of 232 kW, battery storage capacity of 34,021 Ah, a charge controller size of 100 A/560V, and an inverter with a power rating of 60V/75 kW has been designed to meet the load demand. The economic analysis showed a $266,936 life cycle cost, $0.12 per kWh levelized cost of electricity (LCOE), 4-year simple payback time, and a 20.5% internal rate of return (IRR). The PV system is feasible due to its positive net present value (NPV) of $165,322 and carbon savings of 582 tCO2/year.

This work presents a new model to obtain the minimum cost design for a rectangular isolated footing, taking into account that the column is located in any part of the footing. The methodology is developed by integration to obtain the moments, bending shear and punching shear according to the American Concrete Institute ACI 318-14. This document presents the simplified and precise equations of the four moments, four bending shears and one punching shear acting on the footing. Some designs have been developed by the trial and error method to determine the footing dimensions, and later the thickness and steel area of the footing are obtained. Some authors present the minimum cost design for a rectangular isolated footing taking into account that the column is located in the center of gravity of the footing, and other authors present very complex algorithm. Numerical examples are presented to obtain the minimum cost design of rectangular isolated footings under biaxial bending, and some results are compared with those of other authors considering the same conditions. The new model presents a smaller contact area with the soil and a lower design cost than those presented by other authors.

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and Marine resources survey. However, the visibility of the underwater environment and the accuracy of target detection can be affected by complex underwater light changes and unpredictable background noise. To address these issues, we propose an improved underwater target detection algorithm based on YOLOv8n. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region's features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIOU loss function with the Wise-IOU loss function. To conduct our experiments, we create our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on the underwater target detection were 88.6% and 51.8%, respectively, and the improved algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOV8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The improved YOLOV8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The floating-point computation volume of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

The study presented here, through the use of GIS and physical variables, generate a new meth-odology for the calculation of erosivity (Factor R) for the qualitative evaluation of soil loss po-tential (laminar erosion) in the Northwest margin of the municipality of São Francisco, mostly located in the Pardo-MG river basin. The results obtained were statistically correct when com-pared to the traditional R calculation and the laminar erosion methodology. It is worth noting that the new proposal presented here indicated sites of qualitative loss of soil in the same places where there are closed-loop satellite signals of degraded Cerrado, something not identified in the traditional model. As conclusion it can be said that the obtained results were extremely sat-isfactory, being advised more studies to verify this new method proposed here in more places beyond the study area elaborated here

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of the machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man-machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which SEL was found to be 88.04J/mm3. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.

Peste des Petits Ruminants (PPR), a highly contagious viral disease, causes significant economic losses in sheep and goats. Laboratory diagnosis is crucial to disease control and eradication. Since PPRVs have recently adopted new hosts, genetic diversity within isolates of the same lineage is more likely than if they were host-specific, sheep and goats most often. ELISA detects antibodies and antigens in serology. However, mishandling, environmental conditions (temperature and humidity), storage, and sensitivity issues of commercially available ELISA kits prevent rapid PPR virus detection in third-world countries like Pakistan. In the 2020-21 outbreaks in Pakistan, 325 blood samples, 19 swabs, and 6 virus tissue samples of sheep and goats were collected from Gilgit, Islamabad, and Fateh Jang. These virus isolates were submitted to NCBI for partial N gene sequencing. Antigen was prepared from indigenous virus Using semi-purified antigen from PPRV, an indirect ELISA for the detection of PPR antibodies in goat and sheep serum was developed using MW600922 grown in Vero cells. Dilutions of 1:200 serums and 1:32 antigens improve antibody detection. The N-gene-based analysis revealed a better understanding of PPRV genetic characterization. This study develops an RT-PCR assay to detect PPRV by targeting the N-protein gene. This assay offers an accurate and affordable diagnostic tool for PPRV and partial genome sequencing. Results described that PPRV isolates from the current study have 99.73% and 99.4% similarity to previously published Lahore and Faisalabad isolates from Pakistan, respectively. Compared to previous isolates from NCBI data, the virus showed high divergence rates with the Turkish strain. The comparative analysis between Commercial kit (c-ELISA) and I-ELISA revealed that the former had a high sensitivity of 90.60% and specificity of 85.23% compared with the cELISA kit. By comparing I-ELISA and VNT, we find that the current assay is 100% specific and 82.14 % sensitive. Based on these results, serological surveys for PPR antibodies, on a larger scale, in small ruminants can be conducted with indirect ELISA rather than competitive ELISA. Furthermore, the cost and storage efficiency was highly significant because the currently developed method has a low production cost, which is much lesser than the commercially available kit. Our findings demonstrated a significant breakthrough in terms of cost-effectiveness and storage efficiency, and they are highly recommended for developing countries.

The capability of extracting information and analyze it into a common format is essential for performing predictions, comparing projects through cost benchmarking, and for having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of the data makes this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway costs infrastructure data. To validate the suggested approach, data from 23 real historical projects from the client network rail was extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing for costs benchmarking. The presented method proves the benefits of data extraction being able to gather, analyze and benchmark each project in an efficient manner, and deeply understand the relationships and the relevant factors that matter in infrastructure costs.

Evaporating liquid sessile drop deposited on horizontal surface is an important object of applications (printing technologies, electronics, sensorics, medical diagnostics, hydrophobic coatings, etc.) and theoretical investigations (microfluidics, self-assembly of nanoparticle, crystallization of the solute, etc.). The arsenal of formulas for calculating the slow evaporation of an axisymmetric drop of capillary dimensions deposited on a flat solid surface is reviewed. Such characteristics as vapor density, evaporation flux density, total evaporation rate are considered. Exact solutions obtained in the framework of the Maxwellian model, in which the evaporation process of the drop is limited by vapor diffusion from the drop surface to the surrounding air, are presented. The summary covers both well-known results obtained during the last decades and new results published by us in the last few years, but practically unknown to the wide scientific community. The newest formulas, not yet published in refereed publications, concerning exact solutions for a number of specific contact angles are also presented. In addition, new approximate solutions are presented for the first time (total evaporation rate and mass loss per unit surface area per unit time in the whole range of contact angles ), which can be used in modeling without requiring significant computational resources.

Context: Despite the effort put into developing standards for structuring construction cost, and the strong interest into the field. Most construction companies still perform the process of data gathering and processing manually. That provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly on big projects. Additionally, the lack of standardization makes very difficult the cost estimation and comparison tasks. Objective: To create a method to extract and organize construction cost and quantity data into a consistent format and structure, to enable rapid and reliable digital comparison of the content. Method: The approach consists of a two-step method: Firstly, the system implements data mining to review the input document and determine how it is structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data is processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A big variety of information coming from real historical projects has been successfully extracted and processed into a common format with 97.5% of accuracy, using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusion: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking.

Nanostructured transition metal nitrides (TMNs) have been considered a promising substitute for precious metal catalysts toward ORR due to their multi-electron orbitals, metallic properties, and low cost. To design TMNs catalysts with high catalytic activity toward ORR, the intrinsic features of the influencing factor on the catalytic activity toward ORR of nanostructured TMNs need to be investigated. In the paper, titanium nitride (TiN), zirconium nitride (ZrN), and hafnium nitride (HfN) nanoparticles (NPs) are high-efficiency and one-step synthesized by the direct current arc plasma. TiN, ZrN, and HfN NPs with an oxidation layer are applied as the catalysts of hybrid sodium-air batteries (HSABs). The effect of composition and structure attributes of TMNs on ORR catalysis is defined as follows. (ⅰ) Composition effect. With the increase of the oxygen content, the catalytic ORR capability of TMNs decreases progressively due to the reduction of oxygen adsorption capacity; (ⅱ) Structure effect. The redistribution of the density of states (DOS) of ZrN indicates higher ORR activity than TiN and HfN. HSABs with ZrN exhibit an excellent cyclic stability up to 137 cycles (about 140 h), an outstanding rate performance, and a specific capacity of 2817 mAh·g−1 at 1.0 mA·cm−2.

This paper presents a theoretical evaluation of the prices of mixed briquettes produced from co-conut shells (CCS), banana peels (BNP), rattan waste (RWT), and sugarcane bagasse (SGC), and, on the other hand, an analysis of the economic viability of their use as a replacement for conventional household fuels (Liquefied Petroleum Gas, fuelwood, and wood charcoal) in house-holds in Cameroon. The investigation was carried out using the Life Cycle Cost method on a typical household over a ten-year period with annual cooking energy requirements of 950 kWhth. The SGC-CCS and SGC-RWT mixed briquettes with a ratio higher than 7.75% and 11.1%, respectively, have prices lower than 0.063 €/kWhth. The Present Value of the Net Benefit is posi-tive for the use of SGC-CCS and SGC-RWT mixed briquettes. The results show that by making the right mixes of residues, it is possible to obtain biomass briquettes that are less expensive than conventional fuels.

We study the robustness of the cμ-rule for the optimal allocation of a resource consisting of one unreliable server to parallel queues with two different classes of customers. The customers in queues can be served with respect to a FCFS retrial discipline, when the customers at the head of queues repeatedly tries to occupy the server in a random time. It is proved that for the scheduling problem in the system without arrivals the cμ-rule minimizes the total average cost. For the system with arrivals it is difficult directly to prove the optimality of the same policy with explicit relations. We derived for an infinite-buffer model a static control policy that also prescribes for the certain values of system parameters the service exclusively for the class-i customers if both of queues are not empty with the aim to minimize the average cost per unit of time. It is also shown that in a finite-buffer case the cμ-rule fails.

Blasting pattern optimization is an attempt to optimize blast design parameters aiming to achieve optimum fragmentation, reduce mining operating costs as well as environmental side consequences. The present study aims to propose a multi-objective optimization model, employing artificial intelligence and metaheuristics, to simultaneously minimize the mine-to-crusher operating costs and the impact of blasting consequences including fly-rock and back-break. To achieve the purpose of the study, a multi-variable regression model was developed to model total costs from drilling to crushing. In addition to the costs, multilayer perception neural networks were implemented to predict the blast-induced back-break and fly-rock as a function of collected variables such as number of holes, hole length, burden, spacing, hole slope, stemming, blasted rock per hole, powder factor, and charge per delay. High-precise estimations for both back-break and fly-rock were achieved with the average of 99% coefficient of determination for train, test and validation data sets. Then, the developed regression model and the neural networks were used in an optimization framework, employing Grasshopper algorithm, to find the optimum blast design satisfying practical constraints. The proposed model was tested on a lead-zinc open-pit mine, where 1032 blasting patterns were recorded and analyzed. The results of the optimization model provide a Pareto set of solutions, such that any of these solutions can be implemented according to the strategy of the mining operation management team. The blast pattern with the lowest cost, result in a relatively high fly-rock and back-break, while the pattern with low fly-rock and back-break raises the cost by 20.13 % compared the minimum cost blast design.

A novel prediction model for crack development of reinforced concrete (RC) piles with localized chloride corrosion in marine environment is proposed. A discrete method is used to solve the corrosion pit radius model and a crack extension model is developed to investigate the initiation and extension of cracks. The maximum corrosion degree of reinforced concrete pile is predicted according to the limit crack criterion, and finally a sensitivity analysis is carried out on the important parameters of crack extension. The results show that the radius of corrosion pit, the depth corrosion pit and cross-sectional area loss of reinforcement gradually increase as the corrosion level increases. The loss of local reinforcement section at crack initiation decreases with the increase of the ratio of concrete cover to initial diameter, and increases with the increase of pitting factor. The required pit depth for reinforcement cracking increases with the increase of the ratio of concrete cover thickness to diameter. The loss of cross-sectional area of reinforcement and the radius of corrosion pit increase with the increase of initial diameter of reinforcement. Increasing the pitting factor will reduce the pit depth and make the crack width develop faster before reaching the limit crack width. Increasing the concrete cover thickness can provide an improvement in the propagation of cracks. A comparative analysis shows that the localized corrosion pattern is more in conformity with marine engineering practice.

Chlorhexidine digluconate (CHX) has shown the ability to significantly reduce inflammation and marginal bone loss (MBL) at 1-year follow-up but limited data exists regarding its long-term efficacy in peri implant stability. The objective was to compare the long-term effects of a placebo gel (16 patients in Group A) or a 0.20% CHX gel (15 patients in Group B) used at each previous surgical and prosthetic stage. Follow-ups were conducted in 2022, investigating by biological, radiological, clinical, mechanical evaluation the long-term effects. Data were statistically analyzed. The research achieved a 96.7% implant success rate over five years, but 41.9% of patients didn't attend annual oral hygiene check-ups. The average MBL was 1.04 ± 0.39 mm, with no significant differences between the two groups. Notably, patients who attended regular periodontal check-ups experienced significantly less MBL than those who didn't (p &lt;0.05). At five years, the effects of CHX were reduced, with both groups showing moderate bone loss. The results suggest early disinfection could enhance both short and long-term outcomes. In fact, patient with initial minor MBL preserve this advantage also at 5 years of follow-up. Additionally, the data underscore the importance of annual check-ups in early detection and management of biological and mechanical complications.

The epistemology of the traditional risk assessment examined has a taxonomy based upon deterministic, behaviouristic and compatibilist methodologies which are likely driven by neo-liberal market related requirements for bigger, faster cheaper wind-turbines. Analysis of available risk assessments suggested that there is no conscious effort to deceive the reader although the intended audience is unclear. The language, and scoring mechanisms utilized indicates the presence of conformity bias, confirmation bias, and numerical inconsistency leading to data ossification. By reverse engineering risk assessment content, Pragmatic and Social Psychological aspects can be investigated, and the adequacy of the document can be evaluated using evidence-based models.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective category based on input attributes. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that is available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

Installations of decentralised Renewable Energy Systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for decentralised renewable generation and hybrid storage system to create a Renewable Energy Community (REC), developed in Python. The model implements PV Solar and Wind Turbines combined with a hybrid battery and Regenerative Hydrogen Fuel Cell (RHFC). The electrical service demand is derived using real usage data from a rural island case study location. Cost remuneration is managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective Genetic Algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design results in an LCOE of 0.15€/kWh, reducing costs by over 50% compared with typical EU grid power, with a project IRR of 10.8%, simple return of 9.6%/year, and ROI of 9 years. Emissions output from grid only use is reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

This comprehensive critical review shows how green buildings and energy optimization solutions improve indoor air quality as well as environmental quality (IEQ) and human health. Electricity-efficient green buildings create a better indoor atmosphere and save electricity. Green buildings improve occupant well-being through IAQ, thermal comfort, acoustics, and lighting. Renewable energy, green construction materials, and intelligent technologies increase sustainability and reduce environmental impact. Environmental hazards, architectural design, and societal variables can improve indoor air quality and subsequently reduce health risks linked with poor air quality. The evaluation highlights the pros and cons of low-cost IAQ sensors and equipment. These devices provide valuable insights, but calibration and validation using reference equipment are necessary to ensure accuracy and reliability. Architects, engineers, environmentalists, and policymakers are essential in creating green buildings with high IEQ. Prioritizing occupant comfort, health, and productivity ensures a sustainable and healthy future for future generations. However, continual monitoring and research are needed to improve green building practices and overcome construction problems. Intelligent models for continual on-field calibration of low-cost sensors may improve performance. Green buildings benefit occupants and the environment by optimizing energy use and using IAQ monitoring technologies.

Energy poverty is a severe electrification challenge in Nigeria that has affected the country's economic development. The gap between electricity demand and supply continues to increase, and a large percentage of the populace affected are rural dwellers. With the prevailing electricity inaccessibility in rural, renewable energy sources (RES) present viable option for addressing rural electrification challenge. However, RES like solar have intermittency challenges since they are weather dependent. Thus, a hybrid microgrid system (HMS) comprising solar, hydropower and battery storage was sized as a feasible solution for electrifying rural areas in Nigeria. For the HMS sizing, a site was selected, whose residential load demand was estimated. The renewable energy generation potential for the selected resources at the site as well as grid reliability were assessed. Hybrid Optimisation Model for Multiple Energy Resources (HOMER) was used to size the HMS. An economic analysis was performed to determine the levelised cost of energy (LCOE) and net present cost (NPC). For the sized HMS, two scenarios were considered: off-grid and grid-connected HMS configurations. The results showed that the LCOE of the off-grid and grid-connected configurations are $0.101/kWh and $0.046/kWh, respectively. The NPC of the off-grid and grid-connected configurations are $227,307 and $145,282, respectively.

Machine Learning can be used for social good. In this paper, we discuss how it can be used to combat climate change and facilitate land management and farming in developing countries and in particular in Côte d’Ivoire. This paper explores models that improve land and water management and agricultural farming cultivation to contrast climate change. Côte d’Ivoire is the largest producer of cocoa beans (43%) in the world, but deforestation, lack of rainfall, drought, and climate change threaten crops and the already fragile economy of Ivorian farmers. It is important to combat climate change with methods and techniques that are affordable to the local farmers and also induce positive effects in production. We discuss the use of low-cost sensors to collect data on the soil and open data and open source software to develop AI tools. We show that using deep neural networks (YOLOv5m) is effective for detecting healthy plants and pods of cocoa from damaged ones only using mobile phone images. Focusing on a single land is not enough to combat climate change, which has different causes and involves also knowledge at a higher scale. We propose a new method of forecasting for the analysis of remote sensors. Remote sensor data come from GRACE NASA Mission and ERA5 produced by the Copernicus Climate Change Service at ECMWF. We implement a new deep neural network architecture named CIWA-net. It is based on a Fully Convolutional Neural Network (FCN) [ 1] and it is a U-net like architecture [2]. The aim of CIWA-net is to forecast Total Water Storage Anomalies (TWSA). We show the quality of our model with a comparison to a vanilla Convolutional Neural Network. CIWA-net could be used also for the detection of lands that interfere with agricultural work and yields, such as deserted areas, water-soaking soil areas, zones at risk of desertification, and poor land use. The employment of AI at the service of agriculture can decrease crop losses and waste, lower the inputs onto the soil of fertilizers, responsible for the increase of Greenhouse Gases. It could be useful to help the small farmers (at a local scale) and also the policy-makers and farmers’ cooperatives (at the regional scale) to take valid and coordinated countermeasures to improve the correct use of the lands, helping to contrast and adapt to climate change.

Generalized Bayes is a Bayesian approach based on the learning rate parameter η. In this study, we examine the effect of parameter η on the estimation results considering joint type-I and type-II hybrid censored samples from k exponential populations. In addition to the learning rate parameter, we consider two loss functions, the Linex and general entropy loss functions in the Bayesian approach. Monte Carlo simulations are performed to compare the performances of the estimation results under losses and different values of η. An illustrative example is performed to study the effect of the learning rate parameter and the different losses with different parameters.

: There has been an ever-increasing demand for energy from the past decades. Renewable energy technologies play a major role in satisfying the energy demand as well as in a decreased CO2 emissions. Solar and Wind energies are the major upcoming technologies in renewable energies. Decentralized power production is a system where the energy production and consumption are very close to each other. Microgrids can be decentralized or grid connected and they, with renewable energy sources, encounter a problem of storage as the power production from solar and wind is intermittent. This paper discusses the comparison between using batteries and pumped storage hydropower (PSH) as an energy storage system and the integration of wind and solar PV energy sources. HOMER software simulations are used to obtain optimized renewable energy integration in microgrid and to understand its economic analysis. Two scenarios are run with the model where one considers battery and the other considers PSH to obtain the economic and technical best results of these microgrids. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results show that microgrid with storage of PSH is economical with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh in comparison with batteries solution which has an NPC of 95.2M€ and an LCOE of 0.786 €/kWh. The role of storage is understood by differentiating the data into different seasons using Python for data analysis. Furthermore, sensitivity analysis is made by varying the capital cost multiplier of Solar PV and Wind Turbine to obtain optimal solutions.

Most of the literature on the optimization problem of transportation in supply chain networks (SCN) considered fixed costs (FC). However, in the practical applications of this problem it is important to study the effect of FC. This paper examines the impact of fixed cost increase as well as the variable cost on the optimization of a Two-Stage SCN. To do so, two mathematical models for mixed-integer nonlinear programming are developed. The first model optimizes fixed and variable costs in addition to the opening cost, whereas the second model only optimizes variable and opening costs. To evaluate the effect of considering FC on the optimization supply chain problem, four groups of instances are randomly generated, and solved using Lingo. The results of the two models are compared utilizing the average percentage deviation. In addition, sensitivity analysis is performed to determine the impact of changes in opening and variable costs on the considered optimization problem. The computational results and the sensitivity analysis show that the first model with minimized FC outperforms the second model, which does not consider the FC in minimization and FC affect the optimization.

The reason for excessive multidecade deflections is still unclear after 70 years of the cantilever method that has been applied in building large-span segmental concrete bridges. The failure of the Koror-Babeldaob bridge gives the author a clue that the reason might be the boundary condition. The asymmetric properties of the bearing and prestress tendon draw the author’s attention. To understand the process of stress redistribution, the virtual stiffness method which is derived from the principle of minimum potential energy was created, and the phenomenon the author suggested named Material Inelasticity and Prestress Loss Coupling Effect is discovered. The unexplainable excessive deflection and prestress loss are found in the new load model. The calculation of excessive deflection is discussed. The new model fits the experience well. The optimization design methods and construction key points are proposed.

Breakage and damage of fiber optic cable fibers seriously affect the normal operation of fiber optic networks and it is important to quickly and accurately determine the type and location of faults when they occur. The advantages of wavelet transform in singular signal detection and signal filtering are used to analyze Optical Time Domain Reflectometer (OTDR) curve signal and the fault detection method of fiber communication link with no relay and large span in high altitude area is given, which realizes the accurate detection and location of optical fiber communication link fault events under strong noise. The theoretical analysis is basically consistent with the experimental data. The accuracy of optical fiber fault location has been greatly improved in performance. The method has been directly applied to the field detection of ultra-long optical fiber links in high altitude areas, which has certain significance for the future real-time monitoring of optical fiber cables.