In order to deal with complex risk scenarios involving claims, uncertainty, and investments, we consider the ruin problems in a compound Poisson risk model with liquid reserves and proportional investments and study the expected discounted penalty function under threshold dividend strategies. Firstly, the integral differential equation of the expected discounted penalty function is derived. Secondly, since the closed-form solution of the equation cannot be obtained, a sinc method is used to obtain the numerical approximation solution of the equation. Finally, the feasibility and superiority of the sinc method are illustrated by error analysis. In addition, based on a symmetric jump risk market, we discuss the influence of some parameters on the ruin probability with some examples. This study can help actuaries develop more robust risk management strategies and ensure the long-term stability and profitability of insurance companies. It provides a theoretical basis for actuaries to carry out risk management. Full article

The goal of this study was directed to synthesize a novel class of annulated compounds containing difuro[3,2-c:3′,2′-g]chromene. Friedländer condensation of o-aminoacetyl derivative 3 was performed with some active methylene ketones, namely, 1,3-cyclohexanediones, pyrazolones, 1,3-thiazolidinones and barbituric acids, furnished furochromenofuroquinolines (4,5), furochromenofuropyrazolopyridines (6–8), furochromenofurothiazolopyridines (9,10) and furochromenofuropyridopyrimidines (11, 12), respectively. Also, condensation of substrate 3 with 5-amine-3-methyl-1H-pyrazole and 6-amino-1,3-dimethyluracil, as cyclic enamines, resulted in polyfused systems 13 and 14, respectively. In vitro antimicrobial efficiency of the prepared heterocycles against microbial strains exhibited variable inhibition action, where compound 3 was the most effective against all kinds of microorganisms. A significant cytotoxic activity was seen upon the annulation of the starting compound with thiazolopyridine (9 and 10) as well as pyridopyrimidine moieties (11, 12 and 14). The spectroscopic and analytical results were used to infer the structures of the novel synthesized compounds. Full article

Areas excluded from agricultural production are susceptible to the presence of beaver families. The most significant changes occur during the initial period, when agricultural utilization is abandoned and beavers establish their presence on the land. During this period, some parcels remain uncultivated, while agricultural activities persist in neighboring areas. This situation is accompanied by the destruction of beaver dams, especially during periods of abundant water resources, and notably during intensive fieldwork. The article presents field studies aimed at determining the extent to which constructed and operational beaver dams contribute to changes in groundwater levels in drained peatland areas. In order to protect and sustainably use peat soils, it is necessary to maintain their high moisture content by ensuring a high groundwater level elevation. This can be achieved through the use of existing damming structures in the area (levees, weirs). Beaver dams can also serve a similar function, blocking the outflow of water from peat lands by raising the water level and consequently retaining it naturally. The specific objective was to develop principles for verifying factors influencing the effects of beaver dam construction on groundwater levels in fields within their range of influence. The water table levels within the study area during rainless periods were influenced by water levels in ditches, dependent on beaver activity in the nearby river. Beaver activities, manifested through dam construction, were influenced by periodic water resources in the river, defined by the cumulative monthly precipitation. Factors affecting groundwater levels in rainless periods on the plots also included the distance from the river cross-section and the permeability of soils expressed by the filtration coefficient of the active layer. Beaver dams had the greatest impact on stabilizing the water table in the soil profile closest to the river. Full article

In this paper, an irregular octagonal two-port MIMO patch antenna is designed specifically for New Radio (NR) 5G applications in the mid-band sub-6 GHz. The proposed antenna comprises an irregularly shaped patch antenna equipped with a regular 50-ohm feed line and a parasitic strip line antenna, and is partially grounded. Jeans material serves as a substrate with an effective dielectric constant of 1.6 and a thickness of 1 mm. This material is studied experimentally. The proposed antenna design undergoes analysis and optimization using the ANSYS HFSS tool. Furthermore, the design incorporates the influence of the slot on both the ground plane and the parasitic strip line to optimize performance, enhance isolation, and improve impedance matching among antenna elements. The dimensions of the jeans substrate are 40 mm × 50 mm. The simulated impedance bandwidth ranged from 3.6 GHz to 7 GHz and the measured bandwidth was slightly narrower, from 4.35 GHz to 7 GHz. The simulation results demonstrated an isolation level greater than 12 dB between antenna elements, while the measured results reached 28.5 dB, and the peak gain for this proposed antenna stood at 6.74 dB. These qualities made this proposed antenna suitable for various New Radio mid-band 5G wireless applications within the sub-6 GHz band, such as N79, Wi-Fi-5/6, V2X, and DSRC applications. Full article

Ticks transmit a variety of pathogens to their hosts by feeding on blood. The interactions and struggle between tick pathogens and hosts have evolved bilaterally. The components of tick saliva can directly or indirectly trigger host biological responses in a manner that promotes pathogen transmission; however, host cells continuously develop strategies to combat pathogen infection and transmission. Moreover, it is still unknown how host cells develop their defense strategies against tick-borne viruses during tick sucking. Here, we found that the tick saliva peptide HIDfsin2 enhanced the antiviral innate immunity of mouse macrophages by activating the Toll-like receptor 4 (TLR4) signaling pathway, thereby restricting tick-borne severe fever with thrombocytopenia syndrome virus (SFTSV) replication. HIDfsin2 was identified to interact with lipopolysaccharide (LPS), a ligand of TLR4, and then depolymerize LPS micelles into smaller particles, effectively enhancing the activation of the nuclear factor kappa-B (NF-κB) and type I interferon (IFN-I) signaling pathways, which are downstream of TLR4. Expectedly, TLR4 knockout completely eliminated the promotion effect of HIDfsin2 on NF-κB and type I interferon activation. Moreover, HIDfsin2 enhanced SFTSV replication in TLR4-knockout mouse macrophages, which is consistent with our recent report that HIDfsin2 hijacked p38 mitogen-activated protein kinase (MAPK) to promote the replication of tick-borne SFTSV in A549 and Huh7 cells (human cell lines) with low expression of TLR4. Together, these results provide new insights into the innate immune mechanism of host cells following tick bites. Our study also shows a rare molecular event relating to the mutual antagonism between tick-borne SFTSV and host cells mediated by the tick saliva peptide HIDfsin2 at the tick–host–virus interface. Full article

Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level and variation in accuracy with noise level was determined for 5 machine learning algorithms. These attributes were compared to the traditional method of post-processing and a 6x–36 improvement in model performance was noted, depending on the dataset. No algorithm was a clear winner when considering these 4 criteria, as the lowest-error model (neural network) was also the slowest predictor; the algorithm with the lowest overfitting and fastest prediction time (linear regression) had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble algorithm was judged to be the best tradeoff between these criteria due to its error level, prediction time and low variation of error with noise. For the first time, a machine learning model was validated using a 2-qubit datapoint obtained from an IBMQ quantum computer. The best 2-qubit model predicted within 2% of the actual phase, while the traditional method possessed a 25% error. Full article

Sensing and cognition by homeowners and technicians for home maintenance are prime examples of human–building interaction. Damage, decay, and pest infestation present signals that humans interpret and then act upon to remedy and mitigate. The maintenance cognition process has direct effects on sustainability and economic vitality, as well as the health and well-being of building occupants. While home maintenance practices date back to antiquity, they readily submit to augmentation and improvement with modern technologies. This paper describes the use of networked smart technologies embedded with machine learning (ML) and presented in electronic formats to better inform homeowners and occupants about safety and maintenance issues, as well as recommend courses of remedial action. The demonstrated technologies include robotic sensing in confined areas, LiDAR scans of structural shape and deformation, moisture and gas sensing, water leak detection, network embedded ML, and augmented reality interfaces with multi-user teaming capabilities. The sensor information passes through a private local dynamic network to processors with neural network pattern recognition capabilities to abstract the information, which then feeds to humans through augmented reality and conventional smart device interfaces. This networked sensor system serves as a testbed and demonstrator for home maintenance technologies, for what can be termed Home Maintenance 4.0. Full article

The article describes the selection of a permanent magnet synchronous machine design that could be implemented in a small wind turbine designed by the GUST student organization together with researchers working at the Technical University of Lodz. Based on measurements of the characteristics of available machines, eight initial designs of machines with different rotor designs were proposed. The size of the stator, the number of pairs of poles, and the dimensions of the magnets were used as initial parameters of the designed machines. The analysis was carried out about the K-index, the so-called index of benefits. The idea was to make the selected design as efficient as possible while keeping production costs and manufacturing time low. This paper describes how to select the best design of a permanent magnet synchronous generator intended to work with a small wind turbine. All generator parameters were selected keeping in mind the competition requirements, as the designed generator will be used in the author’s wind turbine. Based on the determined characteristics of the generator variants and the value of the K-index, a generator with a latent magnet rotor was selected as the best solution. The aforementioned K-index is a proprietary concept developed for the selection of the most suitable generator design. This paper did not use optimization methods; the analysis was only supported by the K-index. Full article

Much research has been conducted to determine how hair regeneration is regulated, as this could provide therapeutic, cosmetic, and even psychological interventions for hair loss. The current study focused on the hair growth effect and effective utilization of fatty oil obtained from Bryde’s whales through a high-throughput DNA microarray approach in conjunction with immunohistochemical observations. The research also examined the mechanisms and factors involved in hair growth. In an experiment using female C57BL/6J mice, the vehicle control group (VC: propylene glycol: ethanol: water), the positive control group (MXD: 3% minoxidil), and the experimental group (WO: 20% whale oil) were topically applied to the dorsal skin of the mouse. The results showed that 3% MXD and 20% WO were more effective than VC in promoting hair growth, especially 20% WO. Furthermore, in hematoxylin and eosin-stained dorsal skin tissue, an increase in the number of hair follicles and subcutaneous tissue thickness was observed with 20% WO. Whole-genome transcriptome analysis also confirmed increases for 20% WO in filaggrin (Flg), a gene related to skin barrier function; fibroblast growth factor 21 (Fgf21), which is involved in hair follicle development; and cysteine-rich secretory protein 1 (Crisp1), a candidate gene for alopecia areata. Furthermore, the results of KEGG pathway analysis indicated that 20% WO may have lower stress and inflammatory responses than 3% MXD. Therefore, WO is expected to be a safe hair growth agent. Full article

Thyroid cancer (TC) and thyroid autoimmune disorders (AITD) are among the most common diseases in the general population, with higher incidence in women. Chronic inflammation and autoimmunity play a pivotal role in carcinogenesis. Some studies, indeed, have pointed out the presence of AITD as a risk factor for TC, although this issue remains controversial. Prevention of autoimmune disease and cancer is the ultimate goal for clinicians and scientists, but it is not always feasible. Thus, new treatments, that overcome the current barriers to prevention and treatment of TC and AITD are needed. Alkaloids are secondary plant metabolites endowed with several biological activities including anticancer and immunomodulatory properties. In this perspective, alkaloids may represent a promising source of prophylactic and therapeutic agents for TC and AITD. This review encompasses the current published literature on alkaloids effects on TC and AITD, with a specific focus on the pathways involved in TC and AITD development and progression. Full article

Essential trace elements are micronutrients whose deficiency has been associated with altered fertility and/or adverse pregnancy outcomes, while surplus may be toxic. The concentrations of eight essential trace elements were measured using inductively coupled mass spectrometry (ICP-MS) and assessed with respect to clinical in vitro fertilization (IVF) outcomes in a population of 51 women undergoing IVF with intracytoplasmic sperm injection (ICSI), pre-implantation genetic screening for aneuploidy (PGT-A), and single frozen euploid embryo transfer (SET/FET). Specifically, copper (Cu), zinc (Zn), molybdenum, selenium, lithium, iron, chromium, and manganese were quantified in follicular fluid and whole blood collected the day of vaginal oocyte retrieval (VOR) and in urine collected the day of VOR and embryo transfer. We found that the whole blood Cu/Zn ratio was significantly associated with superior responses to ovarian stimulation. Conversely, the whole blood zinc and selenium concentrations were significantly associated with poor ovarian response outcomes. Higher levels of whole blood zinc and selenium, urinary selenium, lithium, and iron had significant negative associations with embryologic outcomes following IVF. Regarding clinical IVF outcomes, higher urinary molybdenum concentrations the day of VOR were associated with significantly lower odds of implantation and live birth, while higher urinary Cu/Mo ratios on the day of VOR were associated with significantly higher odds of implantation, clinical pregnancy, and live birth. Our results suggest that essential trace element levels may directly influence the IVF outcomes of Spanish patients, with selenium and molybdenum exerting negative effects and copper-related ratios exerting positive effects. Additional studies are warranted to confirm these relationships in other human populations. Full article

Southern Min is generally known for not using classifiers [CL] for expressing definiteness/indefiniteness as it is associated with the bare classifier construction [CL N]. This paper offers evidence from Xiamen Southern Min (XSM) that the use of a specific classifier vs. the general classifier é contributes to referentiality in an alternative way by supporting object identification as it is due to the semantic specificity present in specific classifiers and absent in the general classifier. In a dialogic cognitive experiment adapted from the “Hidden color-chips” task (Enfield and Bohnemeyer 2001), 18 participants had to manipulate their addressees’ attention toward various objects situated in their immediate physical space through language as well as deictic gestures. The objects were associated with different specific classifiers or with the general classifier, and they were arranged according to the factors of (a) distance from speaker, (b) visibility for speaker, and (c) uniqueness (adjacency of similar items). The results show, among other things, that there is a higher tendency to use the specific CL in the [demonstrative CL N] construction if adjacent similar objects [−unique] are too far away from the speaker for clear identification by a demonstrative or a pointing gesture. This is seen as a last-resort strategy for creating contrast. Further corroboration comes from the use of specific classifiers in later mentions after the general CL failed to achieve clear identification. These findings can be situated in the broader context of other languages with classifiers in contrastive function (Thai, Vietnamese, and Ponapean) and they show the relevance of using dialogic texts for modeling classifier selection in contrast to narrative texts. Finally, dialogic contexts may serve as bridging contexts for grammaticalization from numeral classifiers to definiteness markers. Full article

Research on vehicle trajectory prediction based on road monitoring video data often utilizes a global map as an input, disregarding the fact that drivers rely on the road structures observable from their own positions for path planning. This oversight reduces the accuracy of prediction. To address this, we propose the CVAE-VGAE model, a novel trajectory prediction approach. Initially, our method transforms global perspective map data into vehicle-centric map data, representing it through a graph structure. Subsequently, Variational Graph Auto-Encoders (VGAEs), an unsupervised learning framework tailored for graph-structured data, are employed to extract road environment features specific to each vehicle’s location from the map data. Finally, a prediction network based on the Conditional Variational Autoencoder (CVAE) structure is designed, which first predicts the driving endpoint and then fits the complete future trajectory. The proposed CVAE-VGAE model integrates a self-attention mechanism into its encoding and decoding modules to infer endpoint intent and incorporate road environment features for precise trajectory prediction. Through a series of ablation experiments, we demonstrate the efficacy of our method in enhancing vehicle trajectory prediction metrics. Furthermore, we compare our model with traditional and frontier approaches, highlighting significant improvements in prediction accuracy. Full article

Long coherence times at room temperature make the NV center a promising candidate for quantum sensors and quantum computers. The necessary coherent control of the electron spin triplet in the ground state requires microwave $\pi$ pulses in the nanosecond range, obtained from the Rabi oscillation of the m_S spin states of the magnetic resonances of the NV centers. Laboratory equipment has a high temporal resolution for these measurements but is expensive and, therefore, uninteresting for fields such as education. In this work, we present measurement electronics for NV centers that are optimized for microcontrollers. It is shown that the Rabi frequency is linear to the output of the digital-to-analog converter (DAC) and is used to adapt the time length $\pi$ of the electron spin flip, to the limited pulse width resolution of the microcontroller. This was achieved by breaking down the most relevant functions of conventional laboratory devices and replacing them with commercially available integrated components. The result is a cost-effective handheld setup for coherent control applications of NV centers. Full article

Background and Objectives: Age-related macular degeneration (AMD) is one of the leading causes of central vision loss among elderly patients, and its dry form accounts for the majority of cases. Although several causes and mechanisms for the development and progression of AMD have previously been identified, the pathogenesis of this complex disease is still not entirely understood. As inflammation and immune system involvement are strongly suggested to play a central role in promoting the degenerative process and stimulating the onset of complications, we aimed to analyze the frequency of serum anti-retinal (ARAs) and anti-endothelial cell antibodies (AECAs) in patients with dry AMD and to determine their relationship with the clinical features of the disease, notably the area of geographic atrophy (GA). Materials and Methods: This study included 41 patients with advanced-stage dry AMD and 50 healthy controls without AMD, matched for gender and age. ARAs were detected by indirect immunofluorescence using monkey retina as an antigen substrate, and the presence of AECAs was determined using cultivated human umbilical vein endothelial cells and primate skeletal muscle. Results: ARAs were detected in 36 (87.8%) AMD patients (titers ranged from 1:20 to 1:320) and in 16 (39.0%) (titers ranged from 1:10 to 1:40) controls (p = 0.0000). Twenty of the forty-one patients (48.8%) were positive for AECAs, while in the control group, AECAs were present only in five sera (10.0%). The titers of AECAs in AMD patients ranged from 1:100 to 1:1000, and in the control group, the AECA titers were 1:100 (p = 0.0001). There were no significant correlations between the presence of AECAs and disease activity. Conclusions: This study demonstrates a higher prevalence of circulating AECAs in patients with dry AMD; however, no correlation was found between the serum levels of these autoantibodies and the area of GA. Full article

Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation. Full article

Amino acids are organic compounds that enter the protein structure, being involved in the proper functioning of the body. The role of amino acids in the onset of autism spectrum disorder (ASD) is yet to be established. Our aim was to identify correlations between urine amino acids and their derivatives and ASD. Methods: We designed a case–control study that consisted of 75 boys and girls, aged between 2 and 12 years. For amino acid profile, we used urine samples that were analyzed using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Results: Descriptive analysis showed higher values for glutamine, hydroxyproline, tyrosine, aspartic acid, and tryptophan and lower values for serine in the autism group than in the control group. Also, we found that boys with autism had higher values than the boys in the control group for serine, threonine, and aspartic acid. For girls from both groups, we did not find statistically significant values. In terms of age groups, we found significantly higher values for histidine, threonine, valine, methionine, aspartic acid, glutamic acid, alpha amino-adipic acid, sarcosine, alanine, and beta-alanine and significantly lower values for proline for both the autism and control groups under 5 years. Conclusions: The findings of this study support the assumption that amino acids may have a role in the expression of ASD. Full article

The present research introduces a novel concept termed “neutrosophic fuzzy metric space”, which extends the traditional metric space framework by incorporating the notion of neutrosophic fuzzy sets. A thorough investigation of various structural and topological properties within this newly proposed generalization of metric space has been conducted. Additionally, counterparts of well-known theorems such as the Uniform Convergence Theorem and the Baire Category Theorem have been established for this generalized metric space. Through rigorous analysis, a detailed understanding of its fundamental characteristics has been attained, illuminating its potential applications and theoretical significance. Full article

The development of medical image classification models necessitates a substantial number of labeled images for model training. In real-world scenarios, sample sizes are typically limited and labeled samples often constitute only a small portion of the dataset. This paper aims to investigate a collaborative similarity learning strategy that optimizes pseudo-labels to enhance model accuracy and expedite its convergence, known as the joint similarity learning framework. By integrating semantic similarity and instance similarity, the pseudo-labels are mutually refined to ensure their quality during initial training. Furthermore, the similarity score is utilized as a weight to guide samples away from misclassification predictions during the classification process. To enhance the model’s generalization ability, an adaptive consistency constraint is introduced into the loss function to improve performance on untrained datasets. The model achieved a satisfactory accuracy of 93.65% at 80% labeling ratio, comparable to supervised learning methods’ performance. Even with very low labeling ratio (e.g., 5%), the model still attained an accuracy of 74.28%. Comparison with other techniques such as Mean Teacher and FixMatch revealed that our approach significantly outperforms them in medical image classification tasks through improving accuracy by approximately 2%, demonstrating this framework’s leadership in medical image classification. Full article

This paper presents the process of digital data flow in RE/CAD/RP/CAI systems to develop models for planning surgical procedures in the craniofacial area. At the first RE modeling stage, digital data processing, segmentation, and the reconstruction of the geometry of the anatomical structures were performed. During the CAD modeling stage, three different concepts were utilized. The first concept was used to create a tool that could mold the geometry of the cranial vault. The second concept was created to prepare a prototype implant that would complement the anterior part of the mandibular geometry. And finally, the third concept was used to design a customized prototype surgical plate that would match the mandibular geometry accurately. Physical models were made using a rapid prototyping technique. A Bambu Lab X1 3D printer was used for this purpose. The process of geometric accuracy evaluation was carried out on manufactured prototypes of surgical plates made of ABS+, CPE, PLA+, and PETG material. In the geometric accuracy evaluation process, the smallest deviation values were obtained for the ABS plus material, within a tolerance of ±0.1 mm, and the largest were obtained for CPE (±0.2 mm) and PLA plus (±0.18 mm). In terms of the surface roughness evaluation, the highest value of the Sa parameter was obtained for the PLA plus material, which was 4.15 µm, and the lowest was obtained for the CPE material, equal to 3.62 µm. The knowledge of the flow of digital data and the identification of factors determining the accuracy of mapping the geometry of anatomical structures allowed for the development of a procedure that improves the modeling and manufacturing of anatomical structures within the craniofacial region. Full article

Hybrid composite materials have been widely used to advance the mechanical responses of fiber-reinforced composites by utilizing different types of fibers and fillers in a single polymeric matrix. This study incorporated three types of fibers: basalt woven fiber and steel (AISI304) wire meshes with densities of 100 and 200. These fibers were mixed with epoxy resin to generate plain composite laminates. Three fundamental mechanical tests (tensile, compression, and shear) were conducted according to the corresponding ASTM standards to characterize the steel wire mesh/basalt/epoxy FRP composites used as plain composite laminates. To investigate the flexural behavior of the hybrid laminates, various layer configurations and thickness ratios were examined using a design of experiments (DoE) matrix. Hybrid samples were chosen for flexural testing, and the same procedure was employed to develop a finite element (FE) model. Material properties from the initial mechanical testing procedure were integrated into plain and hybrid composite laminate simulations. The second FE model simulated the behavior of hybrid laminates under flexural loading; this was validated through experimental data. The results underwent statistical analysis, highlighting the optimal configuration of hybrid composite laminates in terms of flexural strength and modulus; we found an increase of up to 25% in comparison with the plain composites. This research provides insights into the potential improvements offered by hybrid composite laminates, generating numerical models for predicting various laminate configurations produced using hybrid steel wire mesh/basalt/epoxy FRP composites. Full article

The reduction in PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a pan-European transmission grid model. It is shown that the system’s generation costs are highest when the assets are used only to maximize PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to a 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts for regional peak reduction as a secondary objective. The high congestion level emphasizes that grid expansion needs to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators. Full article