Search Results (361)

The aim of this study was to evaluate the influence of genotype and diet on geese from crossbreeding meat lines Tapphorn (T) and Eskildsen (E). This study was conducted on 240 crossbred geese assigned to two dietary groups: an SBM diet group fed a standard soybean-based diet and an LPS diet group fed a yellow lupin-based diet. Birds were reared under identical management conditions and slaughtered at 17 weeks of age. The following traits were recorded: meat colour (CIELab), pH₂₄, cooking loss, breast and thigh muscle texture (shear force and energy), and sensory traits. The results showed a significant effect of both genotype and diet on meat quality. The LPS diet lowered shear force and energy (by ~11%, p < 0.001), reduced cooking loss in breast muscles (by ~5%, p < 0.001), and improved the juiciness and flavour of thigh muscles. The ET genotype positively influenced the meat colour intensity (lower L*, higher a*), while the lupin-based diet improved technological parameters, especially the water-holding capacity. The results confirm that replacing soybean meal with yellow lupin protein is an effective nutritional strategy that can improve goose meat quality and sustainability without compromising the sensory quality. These outcomes support developing soy-free feeding strategies in goose production to meet consumer expectations and reduce reliance on imported feed. Full article

Background/Objectives: Proper management of food services aimed at preventing malnutrition and weight loss among residents of long-term care facilities is a critical priority. Accordingly, accurate prediction of energy intake requirements is necessary. This study aimed to estimate the energy intake required to prevent weight loss in residents of Japanese long-term care facilities. Methods: Body weight and 12-day dietary intake were measured from residents aged ≥75 years with a body mass index (BMI) < 25.0 kg/m² who were consuming a regular or chopped diet. In the survey, individuals with oral intake were included, while those with swallowing problems, serious illnesses, dietary restrictions, or medications causing appetite loss were excluded. The rate of body-weight loss and the energy intake per kilogram of body weight (kcal/kg BW) during each 6-month period were calculated. The energy intake per kilogram of body weight corresponding to the rate of body-weight loss of 0% was estimated from the regression line between the rate of body-weight loss and energy intake per kilogram of body weight. Results: The data was analyzed for 99 residents (15 men and 84 women, age 89.3 ± 5.0 years, BMI 20.3 ± 2.6 kg/m²). From the regression results in all participants, the energy intake per kilogram of body weight corresponding to the rate of body-weight loss of 0% was 31.4 kcal/kg BW overall and 33.4 kcal/kg BW for those with a BMI < 18.5 kg/m². Conclusions: The calculation of energy intake using a regression line may be able to predict the energy intake required for weight maintenance without using instrumental measurements or estimation equations, especially in the case of underweight individuals. Full article

Accurate monitoring of rice-planting areas underpins food security and evidence-based farm management. Recent work has advanced along three complementary lines—multi-source data fusion (to mitigate cloud and spectral confusion), temporal feature extraction (to exploit phenology), and deep-network architecture optimization. However, even the best fusion- and time-series-based approaches still struggle to preserve fine spatial details in sub-meter scenes. Targeting this gap, we propose an HRNet-CA-enhanced DeepLabV3+ that retains the original model’s strengths while resolving its two key weaknesses: (i) detail loss caused by repeated down-sampling and feature-pyramid compression and (ii) boundary blurring due to insufficient multi-scale information fusion. The Xception backbone is replaced with a High-Resolution Network (HRNet) to maintain full-resolution feature streams through multi-resolution parallel convolutions and cross-scale interactions. A coordinate attention (CA) block is embedded in the decoder to strengthen spatially explicit context and sharpen class boundaries. The rice dataset consisted of 23,295 images (11,295 rice + 12,000 non-rice) via preprocessing and manual labeling and benchmarked the proposed model against classical segmentation networks. Our approach boosts boundary segmentation accuracy to 92.28% MIOU and raises texture-level discrimination to 95.93% F1, without extra inference latency. Although this study focuses on architecture optimization, the HRNet-CA backbone is readily compatible with future multi-source fusion and time-series modules, offering a unified path toward operational paddy mapping in fragmented sub-meter landscapes. Full article

The degradation and loss of arable soils pose significant challenges to global food security, requiring advanced mapping and monitoring techniques to improve soil and crop management. This study evaluates the integration of Unmanned Aerial Vehicles (UAVs) and orbital sensor data for monitoring and quantifying gullies with low-cost data. The research focuses on a gully in southern Brazil, utilizing high-spatial-resolution imagery to analyze its evolution over a 25-year period (2000–2024). Photointerpretation and manual delineation procedures were adopted to define gully shoulder lines, based on low-cost and multiple-spatial-resolution data from Google Earth Pro (GEP), UAVs and conventional aerial photographs. Planimetric, volumetric, climatic, and pedological parameters were assessed and evaluated over time. Field inspections supported our interpretations. The results show that gully expansion can be effectively mapped and monitored by combining high-spatial-resolution GEP data with aerial imagery. The gully area has increased by more than 50% over the past two decades, based on GEP data, which were corroborated by submeter-resolution UAV data. The findings indicate that the erosive process remains active, progressing toward the base level. These results provide critical insights for land managers, policymakers, and agricultural stakeholders to implement targeted soil recovery strategies and mitigate further land degradation. Full article

Alzheimer′s disease (AD) is a chronic neurodegenerative disorder that leads to memory loss and changes in mental and behavioral functions in elderly individuals. A major pathological feature of AD is the aggregation of amyloid-beta (Aβ) peptides, along with oxidative stress, inducing neurocellular apoptosis in the brain. Gobaishi (Galla chinensis), a traditional herbal medicine, has gained considerable attention for its constituents and potent therapeutic properties, particularly its strong inhibitory activity against Aβ fibril formation. In this study, we investigated the anti-Aβ aggregation effects of Gobaishi and its active constituents. We isolated two compounds by employing Thioflavin T (ThT) assay-guided fractionation, which were identified through various spectroscopic methods as pentagalloyl glucose (PGG) and methyl gallate (MG). Evaluation of their anti-Aβ aggregation effects revealed that PGG and MG contribute 1.5% and 0.7% of the activity of Gobaishi, respectively. In addition, PGG demonstrated significantly stronger DPPH radical scavenging activity (EC₅₀ = 1.16 µM) compared to MG (EC₅₀ = 6.44 µM). At a concentration of 30 µM, PGG significantly reduced the Aβ-induced cytotoxicity in SH-SY5Y cell lines compared to MG. Based on these findings, both Gobaishi and its active compound PGG are proposed as promising candidates for further investigation as potent anti-amyloidogenic agents in AD management. Full article

Apparent power and power factor are crucial metrics for evaluating the energy transmission efficiency and reactive power management in power systems. The increasing complexity of power load structures, driven by evolving energy production and consumption models, has intensified the nonlinear and unbalanced characteristics of circuits, presenting significant challenges to accurate apparent power measurement. The IEEE 1459-2010 standard introduces the concept of effective apparent power to enhance the assessment of energy transmission efficiency under non-sinusoidal and unbalanced conditions. However, the absence of a physical standard and a standardized traceability method for effective apparent power results in inconsistent measurement outcomes across instruments. This study proposes a novel method to trace effective apparent power measurements to the International System of Units (SI) benchmarks, based on the loss characteristics of transmission lines. The method includes a comprehensive analysis of measurement uncertainty. Simulation and experimental validation confirm that the proposed traceability circuit can achieve a measurement uncertainty of 0.0110% (coverage factor k = 2), satisfying the engineering requirement of expanded uncertainty U approximately 0.02% (k = 2). These results demonstrate the method’s practical suitability for engineering applications. Full article

Background: Zolbetuximab, a monoclonal antibody targeting claudin-18.2 (CLDN18.2), which was recently approved as first-line treatment for advanced gastric cancer (AGC), presents unique safety challenges, particularly infusion-related gastrointestinal toxicity and hypoalbuminemia. This study aimed to present our experience with zolbetuximab administration in patients with AGC, focusing on the safety and management effectiveness of our adapted protocol in routine clinical practice. Methods: This study presents our single-institution real-world experience implementing a proactive management protocol (“Project VYLOY”) using zolbetuximab to mitigate these toxicities. We adopted a standardized stepwise infusion protocol and antiemetic premedication to reduce infusion-related nausea and vomiting. Patients with CLDN18.2-positive advanced gastric or gastroesophageal junction adenocarcinoma who received zolbetuximab combined with chemotherapy were included. Results: Twenty-four patients were included. The median infusion duration was 215 min, with an interruption rate of 25.0%. In cycle 1, 62.5% experienced infusion-associated adverse events, primarily grade 1 nausea (54%) and vomiting (25%). Hypoalbuminemia (grade ≥ 2) occurred in 57% of first-line patients, potentially linked to zolbetuximab-induced gastritis and gastrointestinal protein loss. Proactive antiemetic support and infusion rate adjustments substantially reduced infusion interruptions in subsequent cycles (10.9%). Patients without prior gastrectomy had higher nausea and vomiting rates, confirming the stomach’s role in mediating toxicity. Conclusions: Our results suggest that proactive management can improve the safety and tolerability of zolbetuximab, especially by reducing infusion-related toxicity in real-world practice. Full article

Atopic dermatitis (AD) is a common chronic skin disease affecting both children and adults. Currently lacking a clinical cure, AD presents significant physical and emotional challenges for patients and their families, substantially impacting their quality of life. This underscores significant unmet needs in AD management and highlights the necessity for developing effective therapeutic applications. Recently, several chlorine-containing active substances with promising pharmacological activity have been discovered in soft corals cultivated through coral farming. Among these, brianolide, isolated from the soft coral Briareum stechei, has shown promising potential. This study investigated brianolide’s regulatory effects on the inflammatory response in atopic dermatitis and its underlying mechanisms. Using an in vitro human keratinocyte cell line (HaCaT) stimulated with tumor necrosis factor-α (TNF-α)/interferon-γ (IFN-γ) to mimic AD inflammation, brianolide was found to inhibit cytokine and chemokine expression via the mitogen-activated protein kinase (MAPK) and nuclear factor kappa-light-chain-enhancer of activated B cell (NFκB)-signaling pathways. In an in vivo animal model of 2,4-Dinitrochlorobenzene (DNCB)-induced AD, brianolide demonstrated anti-inflammatory effects, reducing transepidermal water loss (TEWL), ear thickness, erythema, and epidermal blood flow. These findings provide new insights into brianolide’s activity against AD-related inflammation, elucidate potential mechanisms, and contribute to understanding the pharmacological potential of natural coral products for AD treatment. Full article

The increasing integration of renewable energy sources (RESs) reduces dependence on conventional generators, thereby minimizing the negative environmental impact of fossil fuels. The distributed location of RESs also affects the voltage profiles (voltage values in network nodes) and reduces power losses. The growing number of RESs connected to the network increases the total installed power in the sources in the power system. This contributes to the periodic excess of generated power. It creates the need to limit generation in conventional power plants and to switch off some RESs. This article proposes an original methodology for optimally managing overloads of high-voltage power lines. The combination of the power flow tracking method and metaheuristic optimization allows for the effective elimination of line overloads. The aim of the calculations is to find the optimal power distribution in the selected sources, which provide minimal power limitation. As a result, this means a minimal reduction in the total generation in RESs. In this way, the effect of eliminating line overloads is achieved at the lowest possible cost of power redispatching. On the basis of the IEEE 118 bus test network, computational cases are considered, which are examples of emergency states. Full article

Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R² values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production. Full article

This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an overview not only helps track managers in their decision-making but also facilitates the discussion among other decision-makers in later phases of the track intervention planning process, including line planners, capacity managers, and project managers. The methodology uses data of different levels of detail, discrete state modelling for uncertain deterioration of components, and component-level intervention strategies. It dynamically updates the condition estimates of components by capturing the interaction between deteriorating components using Bayesian filters. It also estimates the risks associated with different types of potential service losses that may occur due to sudden events using fault trees as a function of time and the condition of components. An implementation of the methodology is conducted for a 25 km regional railway network in Switzerland. The results suggest that the methodology has the potential to help track managers early in the intervention planning process. In addition, it is argued that the methodology will lead to improvements in the efficiency of the planning process, improvements in the scheduling of preventive interventions, and the reduction in corrective intervention costs upon the implementation in a digital environment. Full article

Tooth agenesis is among the most prevalent congenital anomalies affecting human dentition, characterized by the developmental absence of one or more teeth. This condition may be present in either syndromic or non-syndromic forms, with significant implications for oral function, aesthetics, and craniofacial development. This narrative review aims to provide a comprehensive overview of tooth agenesis, defining its classification, genetic underpinnings, epidemiological aspects, phenotypic features, and therapeutic approaches. Recent advances in genetic research have identified numerous causative genes, notably EDA, MSX1, WNT10A, and PAX9, each associated with specific patterns of missing teeth and involved in isolated and/or syndromic forms. Additionally, genes such as TSPEAR, LRP6, PITX2, and GREM2 contribute to varying degrees of severity and tooth distribution, often blurring the lines between syndromic and isolated cases. The genotype-phenotype correlations underscore the complexity of the underlying molecular pathways involved in odontogenesis. From a therapeutic perspective, the management of tooth agenesis requires a multidisciplinary approach, often involving orthodontic, prosthetic, and surgical interventions tailored to the severity of tooth loss and patient age. Early diagnosis represents a crucial role in treatment planning, facilitating timely intervention during growth and enhancing long-term outcomes. In conclusion, tooth agenesis remains a complex clinical condition with a strong genetic basis. A patient-centered and interdisciplinary strategy is essential to address both functional and psychosocial needs. Full article

Objective: Various orbital conditions (trauma, autoimmune thyroid disease, tumors, infections, congenital malformations) may lead to a consecutive increase in orbital cavity pressure resulting in orbital compartment syndrome (OCS). OCS is associated with acute loss of visual function and a high risk of permanent damage to the optic nerve (compressive optic neuropathy). Orbital decompression surgery (ODS) is a time-critical procedure that reduces pressure on the optic nerve, thereby improving visual function. The surgical management protocol for orbital decompression is not standardized and varies. Surgical techniques differ in orbital fat decompression, lateral canthotomy, and decompression of the medial orbital wall and floor. This retrospective study aims to evaluate surgery procedures and the outcome of visual function after orbital decompression surgery. Methods: In this retrospective study, we evaluated 28 patients (17 male, 11 female) with orbital compartment syndrome from May 2016 to October 2024. All patients underwent orbital decompression surgery as first-line treatment. Visual acuity (VA), diplopia, and ocular motility were analyzed pre- and postoperatively. Recovery was defined as postoperative improvement of vision, diplopia, and ocular motility. Linear and logistic regression analyses were used to assess the associations between clinically relevant risk factors and primary outcomes. Results: Orbital decompression surgery was performed with a median of 8.40 h (Q1: 4.80, Q3: 24.00) upon occurrence of symptoms. The average preoperative measured VA (logMAR) of the affected eye was 1.0. A total of 46% of the patients were preoperatively categorized as ”blind“ according to the WHO visual impairment categories. A total of 96% of the patients showed preoperative ocular motility impairment. Diplopia was preoperatively present in 46% of the patients. After orbital decompression surgery, postoperative visual acuity improved in 36% of the patients. Ocular motility improved by 67% and diplopia by 62% after ODS. The primary surgery technique was two-wall decompression in 68% (19/28) of the cases, followed by one-wall decompression (21%; 6/28), and three-wall decompression (11%; 3/28). Lateral decompression (82%; 23/28) and medial wall decompression (93%; 26/28) were the primary procedures performed. Orbital floor wall decompression was performed in only 14% (4/28) of the cases. Regression analysis revealed a statistically significant effect of preoperative measured vision on postoperative vision, while accounting for age, sex, and time to surgery. Conclusions: Orbital decompression surgery is the time-sensitive first-line treatment of acute visual function loss in OCS. Our data showed a postoperative improvement in visual acuity in 36% of the patients, along with considerable improvement rates in diplopia and ocular motility. The primary surgery technique was a two-wall decompression approach with lateral wall decompression and medial wall decompression. Center-specific timeline optimization of OCS patients is essential. Full article

Transport networks are crucial for economic growth, yet their sustainability is increasingly threatened by natural hazards. Recent floods in Italy have highlighted the vulnerability of rail and road infrastructure, causing severe damage and economic losses. The Gargano Promontory in northern Apulia has experienced frequent hydrogeological disruptions over the past decade, significantly affecting bridges and the railway network managed by Ferrovie del Gargano s.r.l. (FdG). However, structural interventions are complex, time-consuming, costly, and involve certain risks. To enhance sustainability and comply with railway safety regulations, FdG has adopted non-structural measures to improve hydrogeological risk classification and management. Despite the prevalence of flood events, the existing literature often overlooks crucial technical aspects, which this study addresses. The HYD.RAIL (HYDraulic Risk Assessment for Infrastructure and Lane) research project aims to improve transport infrastructure resilience by refining hydraulic risk assessments and introducing new classification parameters. HYD.RAIL employs a multicriteria approach, integrating parameters defined in collaboration with railway professionals. This paper presents the initial framework, offering a methodology to identify, classify, and manage hydrogeological risks in transportation infrastructure. Compared to standard methods, which lack detailed risk classification, HYD.RAIL enables more precise flood risk mapping. For example, high-risk points were reduced from 37 to 6 locations on Line 1 and from 134 to 50 on Line 2 using HYD.RAIL. This approach enhances flood risk management efficiency, providing railway operators with a more accurate understanding of infrastructure vulnerabilities. Full article

With the increasing emphasis on managing line losses, accurately locating and analyzing abnormal line losses in distribution networks has become a critical challenge in implementing effective loss reduction strategies. Aiming at locating and analyzing abnormal line losses caused by equipment aging in the distribution network, an accurate location method considering topology matching and parameter estimation in the grid is proposed. Firstly, a topology matching model based on a support vector machine in the grid is established to identify the real-time topology connection relationship within the distribution network. The accuracy of SVM is enhanced through an optimized parameter selection strategy. Secondly, a multi-objective optimization model is built employing the operation data collected by the measurement equipment, focusing on voltage and power estimation to form a parameter estimation model. This model focuses on voltage and power estimation, improving the accuracy of parameter estimation compared to single-parameter optimization methods. The weighting coefficient is selected to minimize the solution error. Finally, by comparing the deviation between the estimated values of the branch parameters and the theoretical values, the aging degree of each branch is evaluated, and branches with abnormal line losses are accurately located. The effectiveness of the proposed method is verified using the IEEE 33-bus distribution network, demonstrating its potential for improving the accuracy of identifying abnormal line losses caused by equipment aging and supporting enhanced distribution network management. Full article