Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)

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Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance
Devine, Brigid C.; Dogan, Alan B.; Sobol, Warren M. (MDPI, 2025-04-23)
Optical Coherence Tomography (OCT) has revolutionized the diagnosis and management of retinal diseases, offering high-resolution, cross-sectional imaging that aids in early detection and continuous monitoring. However, traditional OCT devices are limited to clinical settings and require a technician to operate, which poses accessibility challenges such as a lack of appointment availability, patient and family burden of frequent transportation, and heightened healthcare costs, especially when treatable pathology is undetected. With the increasing global burden of retinal conditions such as age-related macular degeneration (AMD) and diabetic retinopathy, there is a critical need for improved accessibility in the detection of retinal diseases. Advances in biomedical engineering have led to innovations such as portable models, community-based systems, and artificial intelligence-enabled image analysis. The SightSync OCT is a community-based, technician-free device designed to enhance accessibility while ensuring secure data transfer and high-quality imaging (6 × 6 mm resolution, 80,000 A-scans/s). With its compact design and potential for remote interpretation, SightSync widens the possibility for community-based screening for vision-threatening retinal diseases. By integrating innovations in OCT imaging, the future of monitoring for retinal disease can be transformed to reduce barriers to care and improve patient outcomes. This article discusses the evolution of OCT technology, its role in the diagnosis and management of retinal diseases, and how novel engineering solutions like SightSync OCT are transforming accessibility in retinal imaging.
Optimizing Maize Agronomic Performance Through Adaptive Management Systems in the Mid-Atlantic United States
Arinaitwe, Unius; Thomason, Wade; Frame, William Hunter; Reiter, Mark S.; Langston, David (MDPI, 2025-04-27)
Maize (corn) (Zea mays L.) yield is influenced by complex factors, including abiotic and biotic stress and inconsistent nutrient use efficiency, which challenge optimal yield. Standard management recommendations often fall short, prompting interest in intensive management strategies within an Adaptive Maize Management System (ACMS). To investigate this, we employed an addition/omission technique within a randomized complete block design (RCBD) to compare standard maize management recommendations with an intensive management protocol aimed at identifying yield-limiting factors. Our intensive management approach combined early-season biostimulant applications with mid-season supplementation of phosphorus (P), potassium (K), and nitrogen (N) at the V7 stage, followed by foliar fungicides and additional foliar N at the R1 stage. Field trials spanned five Virginia locations over 2022 and 2023 under both irrigated and non-irrigated conditions, yielding ten site-years of data. Analysis via ANOVA in JMP^® Version 18 with Dunnett’s test revealed that the intensive management approach significantly increased grain yield in 3 of 10 experiments. Under non-irrigated conditions, the intensive management practices averaged 5.9% higher yield than the standard management check. We observed a higher response to irrigation in standard management check (34%) than in intensive management check (8.9%). Site-specific irrigation impacts ranged from 14% to 61%. Results emphasize site-specific input recommendations for yield enhancement.
Fear/Anxiety and Sleep Deprivation Combine to Predict Courage
Gibbons, Jeffrey A.; McManus, Brenna E.; White, Ella C.; Gibbons, Akihaya M. (MDPI, 2025-05-06)
The current study examined the combined effects of sleep deprivation and anxiety on participants’ willingness to act courageously in both heroic and everyday situations. The participants consisted of 256 undergraduate students between the ages of 18 to 25 years old seeking regular and extra credit for their psychology classes through SONA. Following informed consent, the participants completed demographic questionnaires through Qualtrics, as well as the Depression Anxiety Stress Scale, the Pittsburgh Sleep Quality Index, and an adapted version of the Woodard Pury Courage Scale-23 (WPCS-23). The adapted Woodard Pury Courage Scale-23 measures participants’ willingness to engage in challenging tasks that require either heroic or everyday courage and the fear they would experience when engaging in these tasks. The six measures of courage included willingness to engage in everyday, heroic, and both acts, as well as fear when engaging in these actions. Fear/anxiety by sleep interactions predicted every courage measure except for fear when engaging in daily courageous actions. The results supported the hypothesis that fear/anxiety and poor sleep would combine to predict courage, and their implications are discussed.
Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions
Kumar, Rahul; Sporn, Kyle; Ong, Joshua; Waisberg, Ethan; Paladugu, Phani; Vaja, Swapna; Hage, Tamer; Sekhar, Tejas C.; Vadhera, Amar S.; Ngo, Alex; Zaman, Nasif; Tavakkoli, Alireza; Masalkhi, Mouayad (MDPI, 2025-05-13)
Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. It has also advanced bone regeneration devices and neuroprosthetics, highlighting its innovation capabilities. While AI advancements in orthopedics are exciting, challenges remain, like the need for standardized surgical system validation protocols, assessing ethical consequences of AI-derived decision-making, and using AI with bioprinting for tissue engineering. Future research should focus on proving the reliability and predictability of the performance of AI-pivoted systems and their adoption within clinical practice. This review synthesizes recent developments and highlights the increasing impact of AI in orthopedic bioengineering and its potential future effectiveness in bone care and beyond.
Canopy Transpiration Mapping in an Apple Orchard Using High-Resolution Airborne Spectral and Thermal Imagery with Weather Data
Chandel, Abhilash K.; Khot, Lav R.; Stöckle, Claudio O.; Kalcsits, Lee; Mantle, Steve; Rathnayake, Anura P.; Peters, Troy R. (MDPI, 2025-05-14)
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management.
From Roots to Resilience: Exploring the Drivers of Indigenous Entrepreneurship for Climate Adaptation
Dharmasiri, Indunil P.; Galappaththi, Eranga K.; Baird, Timothy D.; Bukvic, Anamaria; Rijal, Santosh (MDPI, 2025-05-14)
Our study investigates the drivers that foster the emergence of entrepreneurial responses to climate change among Indigenous communities. Indigenous peoples possess distinct worldviews and approaches to enterprise that prioritize community well-being and environmental stewardship over individual profit. Conventional entrepreneurship theories do not adequately capture Indigenous business approaches, leaving a limited understanding of how Indigenous communities merge traditional ecological knowledge with entrepreneurial activities to adapt to climate challenges. Through a systematic literature review (65 articles) and a case study of six Sri Lankan Vedda communities, we identified 15 key drivers that shape Indigenous climate-adaptive ventures and categorized them under five themes: (1) place-based relationships (resource stewardship, territorial connections, environmental risk factors); (2) intergenerational learning (traditional knowledge transfer, adaptation learning, collective experience); (3) community institutions (social networks, institutional support, overcoming the agency–structure paradox); (4) collective capacity (access to information, access to capital, community-oriented entrepreneurial traits); and (5) culturally aligned venture strategies (Indigenous business models, traditional products, local market relationships). Our study demonstrates how Vedda communities integrate entrepreneurship with cultural values to enhance climate resilience. Our research advances the field of Indigenous entrepreneurship while providing insights for policymakers and practitioners to support culturally appropriate climate adaptation strategies that enhance both community well-being and environmental sustainability.
Near-Infrared Reflectance Spectroscopy Calibration for Trypsin Inhibitor in Soybean Seed and Meal
Fletcher, Elizabeth B.; Rosso, M. Luciana; Walker, Troy; Huang, Haibo; Morota, Gota; Zhang, Bo (MDPI, 2025-05-14)
Trypsin inhibitors (TI) are naturally occurring antinutritional factors found in soybean seeds [Glycine max. (L.)] that decrease the growth rate of livestock, causing malnutrition and digestion troubles. The current accurate method to quantify TI levels in soybean seeds or meals is by high-performance liquid chromatography (HPLC); however, it is time-consuming, creating bottlenecks in industrial processing. Establishing a near-infrared reflectance spectroscopy (NIR) model for estimating TI in seeds and meals would provide a more efficient and cost-effective method for breeding programs and feed producers. In this study, 300 soybean lines, both seeds and meals, were analyzed for TI content using HPLC, and calibration models were created based on spectral data collected from a Perten DA 7250 NIR instrument. The resulting models demonstrated robust validation, achieving accuracy rates of 97% for seed total TI, 97% for seed Kunitz TI, and 89% for meal total TI. The findings of this study are significant as no NIR calibration models had previously been developed for TI estimation in soybean seed and meal. These models can be used by breeding programs to efficiently assess their lines and by industry to quickly evaluate their soybean meal quality.
Reproductive Performance and Milk Composition of Sows Fed Diets Supplemented with an Immunomodulator
Estienne, Mark J.; Lee, Jung W.; Niblett, R. Tyler; Humphrey, Brooke D.; Monegue, H. James; Lindemann, Merlin D. (MDPI, 2025-05-15)
A cooperative study involving 189 litters from 114 sows (initial BW of 200.8 ± 37.1 kg) at two experiment stations was conducted to investigate the effects of dietary supplementation with OmniGen-AF (OG) (Phibro Animal Health Co., Teaneck, NJ, USA), a nutritional product formulated to improve immune function of animals, on sow reproductive performance and milk composition. Dietary treatments were (1) corn–soybean meal-based control diets or (2) control diets supplemented with OG at 0.75% (~9 g of OG/100 kg BW/d). Supplementation of diets with OG resulted in lesser (p < 0.05) BW changes of sows during lactation (−12.1 vs. −8.2 kg). Litter sizes for control and OG-fed sows were similar, but sows fed OG-based diets had greater (p < 0.05) litter weight for total born (18.3 vs. 19.3 kg) and weaned (63.2 vs. 67.0 kg) and lactation litter gain (47.8 vs. 50.7 kg). Lactation feed intake for the controls and OG-fed sows (5.32 vs. 5.52 kg/d, respectively) did not differ. Supplementing diets with OG increased lactose content (5.78 vs. 5.84%; p = 0.05) and reduced protein content (4.77 vs. 4.68%; p = 0.04) in sow milk. In conclusion, dietary supplementation with OG at 0.75% reduced weight loss during lactation and improved litter weights with marginal effects on the milk composition of sows.
Guinea Pigs Are Not a Suitable Model to Study Neurological Impacts of Ancestral SARS-CoV-2 Intranasal Infection
Joyce, Jonathan D.; Moore, Greyson A.; Thompson, Christopher K.; Bertke, Andrea S. (MDPI, 2025-05-15)
Neurological symptoms involving the central nervous system (CNS) and peripheral nervous system (PNS) are common complications of acute COVID-19 as well as post-COVID conditions. Most research into these neurological sequalae focuses on the CNS, disregarding the PNS. Guinea pigs were previously shown to be useful models of disease during the SARS-CoV-1 epidemic. However, their suitability for studying SARS-CoV-2 has not been experimentally demonstrated. To assess the suitability of guinea pigs as models for SARS-CoV-2 infection and the impact of SARS-CoV-2 infection on the PNS, and to determine routes of CNS invasion through the PNS, we intranasally infected wild-type Dunkin-Hartley guinea pigs with ancestral SARS-CoV-2 USA-WA1/2020. We assessed PNS sensory neurons (trigeminal ganglia, dorsal root ganglia), autonomic neurons (superior cervical ganglia), brain regions (olfactory bulb, brainstem, cerebellum, cortex, hippocampus), lungs, and blood for viral RNA (RT-qPCR), protein (immunostaining), and infectious virus (plaque assay) at three- and six-days post infection. We show that guinea pigs, which have previously been used as a model of SARS-CoV-1 pulmonary disease, are not susceptible to intranasal infection with ancestral SARS-CoV-2, and are not useful models in assessing neurological impacts of infection with SARS-CoV-2 isolates from the early pandemic.
Case Study: Genetic and In Silico Analysis of Familial Pancreatitis
Sharma, Yash; Good, Deborah J. (MDPI, 2025-05-20)
Background/Objectives: Chronic pancreatitis (CP) is a progressive inflammatory condition of the pancreas that leads to irreversible changes in pancreatic structure. The pancreatic α and β cells secrete hormones such as insulin and glucagon into the bloodstream. The pancreatic acinar cells secrete digestive enzymes that break down macromolecules. When these digestive enzymes do not function properly, maldigestion, malabsorption, and malnutrition may result. Presented here is a case study of an individual newly diagnosed with chronic pancreatitis, along with a genetic analysis of his son and an in-silico analysis of two of the variant proteins. Methods: This study was conducted using human subjects, namely, the proband (father) and his son. Medical genetic testing of the proband (father) identified the presence of two variants in the cystic fibrosis transmembrane receptor gene (CFTR): variant rs213950, resulting in a single amino acid change (p. Val470Met), and variant rs74767530, a nonsense variant (Arg1162Ter) with known pathogenicity for cystic fibrosis. Medical testing also revealed an additional missense variant, rs515726209 (Ala73Thr), in the CTRC gene. Cheek cell DNA was collected from both the proband and his son to determine the inheritance pattern and identify any additional variants. A variant in the human leukocyte antigen (rs7454108), which results in the HLA-DQ8 haplotype, was examined in both the proband and his son due to its known association with autoimmune disease, a condition also linked to chronic pancreatitis. In silico tools were subsequently used to examine the impact of the identified variants on protein function. Results: Heterozygosity for all variants originally identified through medical genetic testing was confirmed in the proband and was absent in the son. Both the proband and his son were found to have the DRB1*0301 (common) haplotype for the HLA locus. However, the proband was also found to carry a linked noncoding variant, rs2647088, which was absent in the son. In silico analysis of variant rs213950 (Val470Met) in CFTR and rs515726209 (Ala73Thr) in CTRC revealed distinct changes in predicted ligand binding for both proteins, which may affect protein function and contribute to the development of CP. Conclusions: This case study of a proband and his son provides additional evidence for a polygenic inheritance pattern in CP. The results also highlight new information on the role of the variants on protein function, suggesting additional testing of ligand binding for these variants should be done to confirm the functional impairments.
Alleviation of Plant Abiotic Stress: Mechanistic Insights into Emerging Applications of Phosphate-Solubilizing Microorganisms in Agriculture
Wang, Xiujie; Li, Zhe; Li, Qi; Hu, Zhenqi (MDPI, 2025-05-21)
Global agricultural productivity and ecosystem sustainability face escalating threats from multiple abiotic stresses, particularly heavy metal contamination, drought, and soil salinization. In this context, developing effective strategies to enhance plant stress tolerance has emerged as a critical research frontier. Phosphate-solubilizing microorganisms (PSMs) have garnered significant scientific attention due to their capacity to convert insoluble soil phosphorus into plant-available forms through metabolite production, and concurrently exhibiting multifaceted plant growth-promoting traits. Notably, PSMs demonstrate remarkable potential in enhancing plant resilience and productivity under multiple stress conditions. This review article systematically examines current applications of PSMs in typical abiotic stress environments, including heavy metal-polluted soils, arid ecosystems, and saline–alkaline lands. We comprehensively analyze the stress-alleviation effects of PSMs and elucidate their underlying mechanisms. Furthermore, we identify key knowledge gaps and propose future research directions in microbial-assisted phytoremediation and stress-mitigation strategies, offering novel insights for developing next-generation bioinoculants and advancing sustainable agricultural practices in challenging environments.
Employing Eye Trackers to Reduce Nuisance Alarms
Herdt, Katherine; Hildebrandt, Michael; LeBlanc, Katya; Lau, Nathan (MDPI, 2025-04-22)
When process operators anticipate an alarm prior to its annunciation, that alarm loses information value and becomes a nuisance. This study investigated using eye trackers to measure and adjust the salience of alarms with three methods of gaze-based acknowledgement (GBA) of alarms that estimate operator anticipation. When these methods detected possible alarm anticipation, the alarm’s audio and visual salience was reduced. A total of 24 engineering students (male = 14, female = 10) aged between 18 and 45 were recruited to predict alarms and control a process parameter in three scenario types (parameter near threshold, trending, or fluctuating). The study evaluated whether behaviors of the monitored parameter affected how frequently the three GBA methods were utilized and whether reducing alarm salience improved control task performance. The results did not show significant task improvement with any GBA methods (F(3,69) = 1.357, p = 0.263, partial η² = 0.056). However, the scenario type affected which GBA method was more utilized (X² (2, N = 432) = 30.147, p < 0.001). Alarm prediction hits with gaze-based acknowledgements coincided more frequently than alarm prediction hits without gaze-based acknowledgements (X² (1, N = 432) = 23.802, p < 0.001, OR = 3.877, 95% CI 2.25–6.68, p < 0.05). Participant ratings indicated an overall preference for the three GBA methods over a standard alarm design (F(3,63) = 3.745, p = 0.015, partial η² = 0.151). This study provides empirical evidence for the potential of eye tracking in alarm management but highlights the need for additional research to increase validity for inferring alarm anticipation.
Ruminally Protected Isoleucine, Leucine, Methionine, and Threonine Supplementation of Low-Protein Diets Improved the Performance and Nitrogen Efficiency of Dairy Cows
Qin, Xiaoli; Lin, Xueyan; Hanigan, Mark D.; Zhao, Kai; Hu, Zhiyong; Wang, Yun; Hou, Qiuling; Wang, Zhonghua (MDPI, 2025-04-24)
This study evaluated the effects of supplementing rumen-protected methionine, threonine, isoleucine, and leucine to low-protein diets on lactating dairy cow performance. Sixty Holstein cows were assigned to one of four dietary treatments in a 9-week randomized complete block design: positive control (16% crude protein diet; 16% CP), negative control (12% CP), 12% CP plus the four essential amino acids (12% CP + EAA), and 14% CP supplemented with the four EAA (14% CP + EAA). The milk protein yield was significantly decreased in the 12% CP group compared to the 16% CP group but was restored to comparable levels with EAA supplementation of both the 12% and 14% CP diets. Dietary nitrogen intake and urinary nitrogen excretion both increased with higher dietary CP levels. Nitrogen utilization efficiency in milk was significantly improved by EAA supplementation, with the highest efficiency observed in the 12% CP + EAA treatment (39.0% vs. 33.3% in the 16% CP diet). Plasma urea levels increased with elevated dietary CP and EAA supplementation. Moreover, EAA supplementation significantly elevated venous methionine levels and showed a tendency to increase venous leucine levels. Additionally, compared to the negative control, EAA supplementation increased concentrations of glucagon and prolactin (p < 0.05). EAA supplementation of low-protein diets, particularly the 14% CP diet, improved the dietary protein efficiency of lactating cows without a concomitant decrease in milk protein yield.
On-Road Evaluation of an Unobtrusive In-Vehicle Pressure-Based Driver Respiration Monitoring System
Jain, Sparsh; Perez, Miguel A. (MDPI, 2025-04-26)
In-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings.
Toward Real-Time Posture Classification: Reality Check
Zhang, Hongbo; Gračanin, Denis; Zhou, Wenjing; Dudash, Drew; Rushton, Gregory (MDPI, 2025-05-05)
Fall prevention has always been a crucial topic for injury prevention. Research shows that real-time posture monitoring and subsequent fall prevention are important for the prevention of fall-related injuries. In this research, we determine a real-time posture classifier by comparing classical and deep machine learning classifiers in terms of their accuracy and robustness for posture classification. For this, multiple classical classifiers, including classical machine learning, support vector machine, random forest, neural network, and Adaboost methods, were used. Deep learning methods, including LSTM and transformer, were used for posture classification. In the experiment, joint data were obtained using an RGBD camera. The results show that classical machine learning posture classifier accuracy was between 75% and 99%, demonstrating that the use of classical machine learning classification alone is sufficient for real-time posture classification even with missing joints or added noise. The deep learning method LSTM was also effective in classifying the postures with high accuracy, despite incurring a significant computational overhead cost, thus compromising the real-time posture classification performance. The research thus shows that classical machine learning methods are worthy of our attention, at least, to consider for reuse or reinvention, especially for real-time posture classification tasks. The insight of using a classical posture classifier for large-scale human posture classification is also given through this research.
Performance Evaluation of Numerical Weather Prediction Models in Forecasting Rainfall Events in Kerala, India
Nitha, V.; Pramada, S. K.; Praseed, N. S.; Sridhar, Venkataramana (MDPI, 2025-03-25)
Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods on life, infrastructure, and agriculture. For accurate forecasting of heavy rainfall events in this region, region-specific evaluations of NWP model performance are very important. This study evaluated the performance of six Numerical Weather Prediction (NWP) models—NCEP, NCMRWF, ECMWF, CMA, UKMO, and JMA—in forecasting heavy rainfall events in Kerala. A comprehensive assessment of these models was performed using traditional performance metrics, categorical precipitation metrics, and Fractional Skill Scores (FSSs) across different forecast lead times. FSSs were calculated for different rainfall thresholds (100 mm, 50 mm, 5 mm). The results reveal that all models captured rainfall patterns well for the lower threshold of 5 mm, but most of the models struggled to accurately forecast heavy rainfall, especially for longer lead times. JMA performed well overall in most of the metrics except False Alarm Ratio (FAR). It showed high FAR, which revealed that it may predict false rainfall events. ECMWF demonstrated consistent performance. NCEP and UKMO performed moderately well. CMA, and NCMRWF had the lowest accuracy either due to more errors or biases. The findings underscore the trade-offs in model performance, suggesting that model selection should depend on the accuracy required or rainfall event prediction capability. This study recommends the use of Multi-Model Ensembles (MME) to improve forecasting accuracy, integrate the strengths of the best-performing models, and reduce biases. Future research can also focus on expanding observational networks and employing advanced data assimilation techniques for more reliable predictions, particularly in regions with complex terrain such as Kerala.
A Quantum Key Distribution Routing Scheme for a Zero-Trust QKD Network System: A Moving Target Defense Approach
Ghourab, Esraa M.; Azab, Mohamed; Gračanin, Denis (MDPI, 2025-03-26)
Quantum key distribution (QKD), a key application of quantum information technology and “one-time pad” (OTP) encryption, enables secure key exchange with information-theoretic security, meaning its security is grounded in the laws of physics rather than computational assumptions. However, in QKD networks, achieving long-distance communication often requires trusted relays to mitigate channel losses. This reliance introduces significant challenges, including vulnerabilities to compromised relays and the high costs of infrastructure, which hinder widespread deployment. To address these limitations, we propose a zero-trust spatiotemporal diversification framework for multipath–multi-key distribution. The proposed approach enhances the security of end-to-end key distribution by dynamically shuffling key exchange routes, enabling secure multipath key distribution. Furthermore, it incorporates a dynamic adaptive path recovery mechanism that leverages a recursive penalty model to identify and exclude suspicious or compromised relay nodes. To validate this framework, we conducted extensive simulations and compared its performance against established multipath QKD methods. The results demonstrate that the proposed approach achieves a 97.22% lower attack success rate with 20% attacker pervasiveness and a 91.42% reduction in the attack success rate for single key transmission. The total security percentage improves by 35% under 20% attacker pervasiveness, and security enhancement reaches 79.6% when increasing QKD pairs. Additionally, the proposed scheme exhibits an 86.04% improvement in defense against interception and nearly doubles the key distribution success rate compared to traditional methods. The results demonstrate that the proposed approach significantly improves both security robustness and efficiency, underscoring its potential to advance the practical deployment of QKD networks.
Reconsidering the Social in Language Learning: A State of the Science and an Agenda for Future Research in Variationist SLA
Gudmestad, Aarnes; Kanwit, Matthew (MDPI, 2025-03-28)
The current paper offers a critical reflection on the role of the social dimension of the second language (L2) development of sociolinguistic competence. We center our discussion of L2 sociolinguistic competence on variationist approaches to second language acquisition (SLA) and the study of variable structures. We first introduce the framework of variationist SLA and offer a brief overview of some of the social, and more broadly extralinguistic, factors that have been investigated in this line of inquiry. We then discuss the three waves of variationist sociolinguistics and various social factors that have been examined in other socially oriented approaches to SLA. By reflecting on these bodies of research, our goal is to identify how the insights from this work (i.e., research couched in the second and third waves of variationist sociolinguistics and in other socially oriented approaches to SLA) could be extended to the study of L2 sociolinguistic competence. We argue that greater attention to the social nature of language in variationist SLA is needed in order to more fully understand the L2 development of variable structures.
SYNGAP1 Syndrome and the Brain Gene Registry
Greco, Melissa R.; Chatterjee, Maya; Taylor, Alexa M.; Gropman, Andrea L. (MDPI, 2025-03-30)
Background: The human brain relies on complex synaptic communication regulated by key genes such as SYNGAP1. SYNGAP1 encodes the GTPase-Activating Protein (SYNGAP), a critical synaptic plasticity and neuronal excitability regulator. Impaired SYNGAP1 function leads to neurodevelopmental disorders (NDDs) characterized by intellectual disability (ID), epilepsy, and behavioral abnormalities. These variants disrupt Ras signaling, altering AMPA receptor transport and synaptic plasticity and contributing to cognitive and motor difficulties. Despite advancements, challenges remain in defining genotype–phenotype correlations and distinguishing SYNGAP1-related disorders from other NDDs, which could improve underdiagnosis and misdiagnosis. Brain Gene Registry: The Brain Gene Registry (BGR) was established as a collaborative initiative, consolidating genomic and phenotypic data across multiple research centers. This database allows for extensive analyses, facilitating improved diagnostic accuracy, earlier interventions, and targeted therapeutic strategies. The BGR enhances our understanding of rare genetic conditions and is critical for advancing research on SYNGAP1-related disorders. Conclusions: While no FDA-approved treatments exist for SYNGAP1-related disorders, several therapeutic approaches are being investigated. These include taurine supplementation, ketogenic diets, and molecular strategies such as antisense oligonucleotide therapy to restore SYNGAP1 expression. Behavioral and rehabilitative interventions remain key for managing developmental and cognitive symptoms. Advancing research through initiatives like the BGR is crucial for refining genotype–phenotype associations and developing precision medicine approaches. A comprehensive understanding of SYNGAP1-related disorders will improve clinical outcomes and patient care, underscoring the need for continued interdisciplinary collaboration in neurodevelopmental genetics.
Evaluation of Flange Grease on Revenue Service Tracks Using Laser-Based Systems and Machine Learning
Rahalkar, Aditya; Mirzaei, S. Morteza; Chen, Yang; Holton, Carvel; Ahmadian, Mehdi (MDPI, 2025-03-31)
This study presents a machine learning approach for estimating the presence and extent of flange-face lubrication on a rail. It offers an alternative to the current empirical and subjective methods for lubrication assessment, in which track engineers’ periodic visual inspections are used to evaluate the condition of the rail. This alternative approach uses a laser-based optical sensing system developed by the Railway Technologies Laboratory (RTL) located at Virginia Tech in Blacksburg, VA, combined with a machine learning calibration model. The optical sensing system can capture the fluorescence emitted by the grease to identify its presence, while the machine learning model classifies the extent of grease present into four thickness indices (TIs), from 0 to 3, representing heavy (3), medium (2), light (1) and low/no (0) lubrication. Both laboratory and field tests are conducted, with the results demonstrating the ability of the system to differentiate lubrication levels and measure the presence or absence of grease and TI with an accuracy of 90%.

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