Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach for the evaluation and planning of NT-DHC systems. The methodology involves the use of a knapsack optimization algorithm to perform a comprehensive analysis of the conditions that make the NT-DHC solution competitive against individual heating and cooling technologies. The algorithm determines the optimal combination of potential extensions that maximizes overall economic value. The results of a case study, which was conducted in Italy, show that NT-DHC is more suitable in dense urban areas, while air-to-water heat pumps are better suited for low heat density zones. This methodology aims to reduce the risks associated with energy demand and provide more certainty about which areas a network can expand into to be competitive. It is targeted at energy planners, utilities experts, energy engineers, and district heating experts who require assistance and guidance in the planning and early stages of designing a NT-DHC system. This method might enable pre-feasibility studies and preliminary design to determine the opportunities and limitations of a system of this kind from an economic and technological perspective. Full article

In this research paper, we investigate the controllability in the $\alpha$-norm of a coupled system of integrodifferential equations with state-dependent nonlocal conditions in generalized Banach spaces. We establish sufficient conditions for the system’s controllability using resolvent operator theory introduced by Grimmer, fractional power operators, and fixed-point theorems associated with generalized measures of noncompactness for condensing operators in vector Banach spaces. Finally, we present an application example to validate the proposed methodology in this research. Full article

Pavement performance models (PPMs) are utilized to predict pavement network conditions which is an essential part of any sustainable pavement management system (PMS). The reliability of a PMS and its outputs is proportional to the reliability of the PPM used. This article describes a mechanistic–empirical pavement performance model based on pavement response parameters—strains calculated in the pavement layers measured by tensometers embedded in the pavement surface and verified by calculations in the elastic half-space model and supplemented by empirical data from long-term pavement performance monitoring and accelerated pavement testing. Hence, the herein described PPM combines pavement serviceability evaluation, pavement bearing capacity, and the physico-mechanistic properties of paving materials. The analytical methods which were used to ascertain the physico-mechanistic characteristics, the material fatigue degradation model, and the surface degradation, unevenness in particular, are described. A comparison of the empirical PPM created in the last century used by the national road administrator to this day and the newly created PPM is presented. The comparison shows the difference in the calculated socio-economic benefits and subsequent cost–benefit analysis results. The comparison shows that the use of the old PPM may have produced false economic evaluation results that have led to poor decision making, partially explaining the unsustainable trend of road network management in our country. Full article

In Li Bai’s poems, the term yao or medicine is frequently employed as an idea-image. The meaning of yao can be further divided into four distinct types, each corresponding to its functions in different contexts. It represents the elixir found on Penglai Island, having the power to elevate a person to immortality; the elixir stolen from the Queen Mother of the West by Heng’E; the immortal herbs pounded by the Jade Rabbit; and the medicine used for treating diseases. In addition, Li Bai’s poems also contain elixir liquid (danye 丹液), potable gold (jinye 金液), and other substances referred to as yao. Unlike specific terms like “cinnabar,” these names are more general in nature. The medicines, their names, and the general terms in poems carry different emotional implications, e.g., his admiration for immortality, and a means to criticize his own time, to express his aspirations and lamentation over the passage of time. The “Yao” also serves as a symbol of healing and nourishment, especially in the context of friendship. All these points deserve to be meticulously explored. Full article

Planar reconstruction detects planar segments and deduces their 3D planar parameters (normals and offsets) from the input image; this has significant potential in the fields of digital preservation of cultural heritage, architectural design, robot navigation, intelligent transportation, and security monitoring. Existing methods mainly employ multiple-view images with limited overlap for reconstruction but lack the utilization of the relative position and rotation information between the images. To fill this gap, this paper uses two views and their relative camera pose to reconstruct indoor scene planar surfaces. Firstly, we detect plane segments with their 3D planar parameters and appearance embedding features using PlaneRCNN. Then, we transform the plane segments into a global coordinate frame using the relative camera transformation and find matched planes using the assignment algorithm. Finally, matched planes are merged by tackling a nonlinear optimization problem with a trust-region reflective minimizer. An experiment on the Matterport3D dataset demonstrates that the proposed method achieves 40.67% average precision of plane reconstruction, which is an improvement of roughly 3% over Sparse Planes, and it improves the IPAA-80 metric by 10% to 65.7%. This study can provide methodological support for 3D sensing and scene reconstruction in sparse view contexts. Full article

Chagas disease is caused by the single-flagellated protozoan Trypanosoma cruzi, which affects several million people worldwide. Understanding the signal transduction pathways involved in this parasite’s growth, adaptation, and differentiation is crucial. Understanding the basic mechanisms of signal transduction in T. cruzi could help to develop new drugs to treat the disease caused by these protozoa. In the present work, we have demonstrated that Fetal Calf Serum (FCS) can quickly increase the levels of both phosphorylated and unphosphorylated forms of T. cruzi DNA polymerase beta (TcPolβ) in tissue-cultured trypomastigotes. The in vitro phosphorylation sites on TcPolβ by protein kinases TcCK1, TcCK2, TcAUK1, and TcPKC1 have been identified by Mass Spectrometry (MS) analysis and with antibodies against phosphor Ser-Thr-Tyr. MS analysis indicated that these protein kinases can phosphorylate Ser and Thr residues on several sites on TcPolβ. Unexpectedly, it was found that TcCK1 and TcPKC1 can phosphorylate a different Tyr residue on TcPolβ. By using a specific anti-phosphor Tyr monoclonal antibody, it was determined that TcCK1 can be in vitro autophosphorylated on Tyr residues. In vitro and in vivo studies showed that phorbol 12-myristate 13-acetate (PMA) can activate the PKC to stimulate the TcPolβ phosphorylation and enzymatic activity in T. cruzi epimastigotes. Full article

Chronic pancreatitis (CP) is a rare but debilitating condition with an 8-fold increased risk of developing pancreatic cancer. In addition to the symptoms that come from the loss of endocrine and exocrine function in CP, the management of chronic pain is problematic. We previously showed that the CCK-receptor antagonist called proglumide could decrease inflammation, acinar-ductal metaplasia, and fibrosis in murine models of CP. We hypothesized that proglumide would be safe and diminish pain caused by CP. A Phase 1 open-labeled safety study was performed in subjects with clinical and radiographic evidence of CP with moderate to severe pain. After a 4-week observation period, the subjects were treated with proglumide in 400 mg capsules three times daily (1200 mg per day) by mouth for 12 weeks, followed by 4 weeks of observation after discontinuation for safety. The results of three pain surveys (Numeric Rating Scale, COMPAT-SF, and NIH PROMIS) showed that the patients had significantly less pain after 12 weeks of proglumide compared to the pre-treatment observation phase. Of the eight subjects in this study, two experienced nausea and diarrhea with proglumide. These side effects resolved in one subject with doses reduced to 800 mg per day. No abnormalities were noted in the blood chemistries. A blood microRNA blood biomarker panel that corresponded to pancreatic inflammation and fibrosis showed significant improvement. We conclude that proglumide is safe and well tolerated in most subjects with CP at a dose of 1200 mg per day. Furthermore, proglumide therapy may have a beneficial effect by decreasing pain associated with CP. Full article

Osteoporosis constitutes a significant public health concern necessitating proactive prevention, treatment, and monitoring efforts. Timely identification holds paramount importance in averting fractures and alleviating the overall disease burden. The realm of osteoporosis diagnosis has witnessed a surge in interest in machine learning applications. This burgeoning technology excels at recognizing patterns and forecasting the onset of osteoporosis, paving the way for more efficacious preventive and therapeutic interventions. Smart walkers emerge as valuable tools in this context, serving as data acquisition platforms for datasets tailored to machine learning techniques. These datasets, trained to discern patterns indicative of osteoporosis, play a pivotal role in enhancing diagnostic accuracy. In this study, encompassing 40 participants—20 exhibiting robust health and 20 diagnosed with osteoporosis—data from force sensors embedded in the handlebars of conventional walkers were gathered. A windowing action was used to increase the size of the dataset. The data were normalized, and k-fold cross-validation was applied to assess how well our model performs on untrained data. We used multiple machine learning algorithms to create an accurate model for automatic monitoring of users’ gait, with the Random Forest classifier performing the best with 95.40% accuracy. To achieve the best classification accuracy on the validation dataset, the hyperparameters of the Random Forest classifier were further adjusted on the training data. The results suggest that machine learning-based automatic monitoring of gait parameters could lead to accurate, non-laborious, cost-effective, and efficient diagnostic tools for osteoporosis and other musculoskeletal disorders. Further research is needed to validate these findings. Full article

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation. Full article

A device called a heat exchanger is used to exchange heat transfer between two fluids with different temperatures. Because of its durability and ability to handle high-pressure application, the concentric double pipe heat exchangers are widely utilized for numerous industrial applications. To conserve pumping power energy, many researchers were involved in study of the nanoparticles to be embedded in the fluid, which will enrich the fluid thermal conductivity and surface area. This article demonstrates the flow characteristics and convective heat transfer of nanofluids containing 0.2, 0.4 and 0.6 of vol% TiO₂ nanoparticles dispersed in water under turbulent conditions, which mainly can be used for cooling nuclear reactors applications. Reynolds numbers varying from 4000 to 18,000 are examined numerically. The convective heat transfer coefficient results of the nanofluid agree well against experimental data, which are slightly more than that of base water at 1.94%. The results of the numerical model showed that the convective heat transfer coefficient of nanofluids will increase when the Reynolds and volume fraction increases. By increasing the temperature of the annular hot water, the heat transfer rate will increase, showing no major impact to the convective heat transfer coefficient of nanofluids. A generalised solution predicting the convective heat transfer coefficient for extensive nanoparticle materials is proposed. The conclusion of the empirical equation is tested among published data and the results are highly congruent, confirming the strength of the gamma equation. Full article

Berberis microphylla, commonly known as calafate, is one of the most promising species of Chilean Patagonia to be domesticated, due to its anthocyanin-rich berries. The main aim of this study was to understand the genetic structure of the wild populations of B. microphylla in the main regions where it grows and its relationship with phenolic secondary metabolite profiles. Ripe berry samples and leaves were collected from the Aysén and Magallanes regions. Genetic analyses were carried out using 18 microsatellite markers. Phenolic compounds were extracted from the ripe fruits and were quantified using high-performance liquid chromatography (HPLC). Their antioxidant capacity was determined according to the Trolox equivalent antioxidant capacity (TEAC) assay. Total phenols were measured as their absorbance at a wavelength of 280 nm. Both the genetic and chemometric data were explored using unsupervised and supervised methods. The genetic markers suggest the existence of three groups, two of them corresponding to the Aysén and Magallanes samples, and the third corresponding to Chile Chico (a district in Aysén), which was the most divergent of the three. Similar results were observed in the phenolic profile obtained with chemometric analysis, with the same samples forming a separate third group. The differentiation achieved using the genetic and chemical data may be the result of intrinsic genetic differences, environmental effects on fruit maturity, or the sum of both factors. These are all points to consider in the domestication of this valuable species by selecting individuals with desirable traits and contrasting phenotypes. Full article

This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages. Full article

Knowledge of a person’s level of skin pigmentation, or so-called “skin tone”, has proven to be an important building block in improving the performance and fairness of various applications that rely on computer vision. These include medical diagnosis of skin conditions, cosmetic and skincare support, and face recognition, especially for darker skin tones. However, the perception of skin tone, whether by the human eye or by an optoelectronic sensor, uses the reflection of light from the skin. The source of this light, or illumination, affects the skin tone that is perceived. This study aims to refine and assess a convolutional neural network-based skin tone estimation model that provides consistent accuracy across different skin tones under various lighting conditions. The 10-point Monk Skin Tone Scale was used to represent the skin tone spectrum. A dataset of 21,375 images was captured from volunteers across the pigmentation spectrum. Experimental results show that a regression model outperforms other models, with an estimated-to-target distance of 0.5. Using a threshold estimated-to-target skin tone distance of 2 for all lights results in average accuracy values of 85.45% and 97.16%. With the Monk Skin Tone Scale segmented into three groups, the lighter exhibits strong accuracy, the middle displays lower accuracy, and the dark falls between the two. The overall skin tone estimation achieves average error distances in the LAB space of $16.40\pm 20.62$. Full article

The pulsed- and steady-pedestal paradigms were designed to track increment thresholds (ΔC) as a function of pedestal contrast (C) for the parvocellular (P) and magnocellular (M) systems, respectively. These paradigms produce contrasting results: linear relationships between ΔC and C are observed in the pulsed-pedestal paradigm, indicative of the P system’s processing, while the steady-pedestal paradigm reveals nonlinear functions, characteristic of the M system’s response. However, we recently found the P model fits better than the M model for both paradigms, using Gabor stimuli biased towards the M or P systems based on their sensitivity to color and spatial frequency. Here, we used two-square pedestals under green vs. red light in the lower-left vs. upper-right visual fields to bias processing towards the M vs. P system, respectively. Based on our previous findings, we predicted the following: (1) steeper ΔC vs. C functions with the pulsed than the steady pedestal due to different task demands; (2) lower ΔCs in the upper-right vs. lower-left quadrant due to its bias towards P-system processing there; (3) no effect of color, since both paradigms track the P-system; and, most importantly (4) contrast gain should not be higher for the steady than for the pulsed pedestal. In general, our predictions were confirmed, replicating our previous findings and providing further evidence questioning the general validity of using the pulsed- and steady-pedestal paradigms to differentiate the P and M systems. Full article

Climate change is a notable challenge for agriculture as it affects crop productivity and yield. Increases in droughts, salinity, and soil degradation are some of the major consequences of climate change. The use of microorganisms has emerged as an alternative to mitigate the effects of climate change. Among these microorganisms, dark septate endophytes (DSEs) have garnered increasing attention in recent years. Dark septate endophytes have shown a capacity for mitigating and reducing the harmful effects of climate change in agriculture, such as salinity, drought, and the reduced nutrient availability in the soil. Various studies show that their association with plants helps to reduce the harmful effects of abiotic stresses and increases the nutrient availability, enabling the plants to thrive under adverse conditions. In this study, the effect of DSEs and the underlying mechanisms that help plants to develop a higher tolerance to climate change were reviewed. Full article

The effects of pineapple residue (PR) on fermentation quality, chemical composition, and bacterial community of corn straw (CS) silage were evaluated. CS was ensiled with 0% control group (CON), 15% (P1), 30% (P2), and 45% (P3) PR on a fresh matter (FM) basis for 45 days. P3 had lower dry matter (DM) and crude protein (CP) contents but higher ammonia-N (NH₃-N) content than the other three groups (p < 0.05). Compared with the other groups, P1 had lower a pH and higher lactic acid and acetic acid contents (p < 0.05). The lactic acid bacteria count in P1 was higher than in P2 and P3 (p < 0.05); the number of yeast in P2 was higher than in the other groups (p < 0.05). With the increasing proportion of PR addition, the relative abundance of Lactobacillus gradually increased, and the dominant genus in P3 was Acetobacter. In summary, the addition of PR can improve the quality of CS silage, and the optimum addition ratio for PR was 15% on a FM basis. Full article

Denture stomatitis (DS) is a very common disease in wearers of removable complete and partial dentures with a worldwide prevalence in the range of 20–67%. Both industrially developed and impoverished nations are affected by the illness. DS is often associated with ill-fitting dentures or a fungal infection with Candida spp. Candida is normally found in the oral cavity microbiota, but it can be harmful to the health of elderly people with underlying diseases. Therefore, the purpose of the present study is to offer the most recent information about the epidemiology, etiology, and global distribution of Candida species associated with DS through a systematic review. Several databases, including Medline, Web of Science, and Scopus, were used to conduct an extensive search of the literature published in the previous 20 years. The selection of studies was performed by two authors. The extracted data were as follows: author, year of publication, country, sample, frequency of DS, method of diagnosing stomatitis, species of Candida, risk factors, and etiology of the disease. The JBI Critical appraisal tools were used to assess the quality of the studies. Eventually, twenty-eight studies were included in the systematic review. Twenty-one studies investigated DS, while seven studies examined Candida colonization in patients using removable dentures. The results show that the main causes of DS include the type of dentures, continuous wearing of dentures, and the formation of a Candida biofilm, which is facilitated by poor dental hygiene. Additionally, previous studies have pinpointed the significance of the salivary flow, saliva composition, and salivary pH. The findings of the current review indicate that it is crucial to monitor denture wearers for the appearance of DS, especially the patients whose immunity has been impaired due to a systemic condition. Finally, frequent follow-ups should include a clinical examination and microbial swabs of the palatal mucosa and the mucosal surface of the denture. Full article

The objective of this work is to compare the levels of bioactive compounds in fresh and freeze-dried basil leaves (Ocimum basilicum), using methodological procedures that follow internationally recommended and accepted standards. The results show comparisons of bioactives between fresh and freeze-dried leaves, respectively, with results expressing the average levels of vitamin C (9.50–63.3 mg/100 g); total polyphenols (1.8–3.9 mgEAG/g); flavonoids (0.73–1.78 mg/g); chlorophyll a (2287.8–1003.8 μg/100 g); chlorophyll b (2606–2287 μg/100 g) and total carotenoids with averages of 16.71–20.6 mg/100 g). Regarding color, there was variation in the parameters L*, a*, and b* of the dry sample, but the tendency towards green e (a+) and yellow (b−) was maintained. Infrared analysis (FTIR) demonstrated the presence of functional groups related to cellulose, hemicellulose, and lignin. Thermogravimetry (TG/DTG) shows stability in the range of 234 °C, showing a more intense mass loss at 294.6 °C. Given the data, it is possible to infer that the application of freeze-drying produces few changes in bioactive compounds and chemical groups and maintains good thermal stability, proving to be a viable alternative to increasing the commercialization of basil leaves, as it prolongs their useful life, and increases the forms of food applications. Full article

Recent advancements in biomimetics have spurred significant innovations in prosthetic limb development by leveraging the intricate designs and mechanisms found in nature. Biomimetics, also known as “nature-inspired engineering”, involves studying and emulating biological systems to address complex human challenges. This comprehensive review provides insights into the latest trends in biomimetic prosthetics, focusing on leveraging knowledge from natural biomechanics, sensory feedback mechanisms, and control systems to closely mimic biological appendages. Highlighted breakthroughs include the integration of cutting-edge materials and manufacturing techniques such as 3D printing, facilitating seamless anatomical integration of prosthetic limbs. Additionally, the incorporation of neural interfaces and sensory feedback systems enhances control and movement, while technologies like 3D scanning enable personalized customization, optimizing comfort and functionality for individual users. Ongoing research efforts in biomimetics hold promise for further advancements, offering enhanced mobility and integration for individuals with limb loss or impairment. This review illuminates the dynamic landscape of biomimetic prosthetic technology, emphasizing its transformative potential in rehabilitation and assistive technologies. It envisions a future where prosthetic solutions seamlessly integrate with the human body, augmenting both mobility and quality of life. Full article

Fiber-reinforced cement composites, particularly those incorporating natural fibers like cellulose, have gained attention for their potential towards more sustainable construction. However, natural fibers present inherent deficiencies in mechanical properties and can benefit from hybridization with carbon fibers. This study focuses on the incorporation of cellulose and carbon fibers, in varying contents, into fibrocement composites, employing a Response Surface Methodology (RSM) to optimize the material characteristics. The methodology involves testing, encompassing flexural tensile, compression, and fracture toughness tests. The results indicate an increasing trend in flexural strength for higher carbon fiber content, peaking near 5%. A plateau in flexural strength is observed between 1.2% and 3.6% carbon fiber content, suggesting a range where mechanical properties stabilize. Compressive strength shows a plateau between 1.2 and 3.6% and reaches its highest value (≈33 MPa) at a carbon fiber content greater than 4.8%, and fracture toughness above 320 MPa·m^1/2 is achieved with carbon fiber content above 3.6%. This study offers insights into optimizing the synergistic effects of cellulose and carbon fibers in fibrocement composites. Full article