University of Manila

Supercapacitors play a vital role as energy storage devices, filling the performance gap between dielectric capacitors and batteries. The present review critically discusses the synergistic integration of current collectors, electrode materials, and electrolytes for the optimum performance of supercapacitors. The review systematically discusses the transition metal sulfides, oxides, and phosphates, as well as the conducting polymer and carbonaceous materials, considering the charge storage mechanisms and material structural engineering. The review also focuses on the development of current collectors from two-dimensional foils (Cu, Al, Ti) to three-dimensional porous foams (Ni, C, Cu). The review also presents the developments in electrolytes, such as the water-in-salt and hybrid electrolytes, which enable the voltage window to extend beyond 2.0 V. The review uniquely combines the physical aspects of the materials with the recent developments in artificial intelligence (AI) and machine learning (ML) for the optimum performance of the supercapacitors. The techno-economic and sustainability aspects of the materials are also discussed for the development of the next generation of supercapacitors.

Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic physics; others maintain geometric consistency but fail when subjected to closed-loop planning. This disconnect exposes a critical gap: the field evaluates how real generated worlds appear, but rarely whether they behave realistically. We introduce WorldLens, a unified benchmark that measures world-model fidelity across the full spectrum, from pixel quality and 4D geometry to closed-loop driving and human perceptual alignment, through five complementary aspects and 24 standardized dimensions. Our evaluation of six representative models reveals that no existing approach dominates across all axes: texture-rich models violate geometry, geometry-aware models lack behavioral fidelity, and even the strongest performers achieve only 2-3 out of 10 on human realism ratings. To bridge algorithmic metrics with human perception, we further contribute WorldLens-26K, a 26,808-entry human-annotated preference dataset pairing numerical scores with textual rationales, and WorldLens-Agent, a vision-language evaluator distilled from these judgments that enables scalable, explainable auto-assessment. Together, the benchmark, dataset, and agent form a unified ecosystem for assessing generated worlds not merely by visual appeal, but by physical and behavioral fidelity.

3D object affordance grounding aims to identify regions on 3D objects that support human-object interaction (HOI), a capability essential to embodied visual reasoning. However, most existing approaches rely on static visual or textual cues, neglecting that affordances are inherently defined by dynamic actions. As a result, they often struggle to localize the true contact regions involved in real interactions. We take a different perspective. Humans learn how to use objects by observing and imitating actions, not just by examining shapes. Motivated by this intuition, we introduce video-guided 3D affordance grounding, which leverages dynamic interaction sequences to provide functional supervision. To achieve this, we propose VAGNet, a framework that aligns video-derived interaction cues with 3D structure to resolve ambiguities that static cues cannot address. To support this new setting, we introduce PVAD, the first HOI video-3D pairing affordance dataset, providing functional supervision unavailable in prior works. Extensive experiments on PVAD show that VAGNet achieves state-of-the-art performance, significantly outperforming static-based baselines. The code and dataset will be open publicly.

Dalbavancin is a parenteral long-acting lipoglycopeptide antibiotic that is FDA approved for acute bacterial skin and soft tissue infections (ABSSSI). It is effective against gram positive organisms including MSSA/MRSA, Streptococcus, and vancomycin susceptible Enterococcus. Its long half-life provides effective 7-day therapy with a single IV infusion. This unique quality is beneficial for patients whose cellulitis is severe enough to typically warrant initial IV therapy, and those who are unlikely to adhere to medications prescribed due to homelessness, lack of adequate insurance coverage/access to medications and people who inject drugs.University of Maryland Medical System Dalbavancin Use GuidelineGuideline used to establish appropriateness of dalbavancin usage.TableCost Analysis We conducted a 6-month pilot from April 2024 to October 2024 including the ED at the University of Maryland (UM)– Upper Chesapeake Health, a community-based hospital with 316 licensed beds along with our affiliated, free-standing ED, Aberdeen Medical Center. Adult patients with a diagnosis of cellulitis without sepsis were evaluated by an ED provider in consultation with an ID provider when available, using specified criteria to determine eligibility for dalbavancin infusion. If a patient met criteria, a 1-time 1,500 mg dose was administered with subsequent discharge from the ED. Data were retrospectively collected and evaluated at the end of the pilot. Overall Analysis A total of 30 patients met criteria and received dalbavancin in our ED during the pilot. Our dalbavancin pathway avoided 25 admissions. We calculated a savings of 81 patient days during the 6-month pilot. Although we found a net cost increase of $717 per patient (vs inpatient admission), there was an overall savings as reduced cellulitis admissions created bed availability for other patient admissions and helped reduce the number of ED boarders. A single-dose dalbavancin infusion given to eligible patients in the ED avoids admissions to the hospital for patients with ABSSSI with suspected or documented gram-positive organisms. Cost savings from avoiding hospital stay did not offset the cost of dalbavancin in our hospital. However, hospitals with 340B pricing are expected to have cost saving in addition to reducing hospital admissions. Dalbavancin should be judiciously used, and alternative oral antibiotic options should be pursued when appropriate. All Authors: No reported disclosures

Large-scale biodiversity monitoring platforms increasingly rely on multimodal wildlife observations. While recent foundation models enable rich semantic representations across vision, audio, and language, retrieving relevant observations from massive archives remains challenging due to the computational cost of high-dimensional similarity search. In this work, we introduce compact hypercube embeddings for fast text-based wildlife observation retrieval, a framework that enables efficient text-based search over large-scale wildlife image and audio databases using compact binary representations. Building on the cross-view code alignment hashing framework, we extend lightweight hashing beyond a single-modality setup to align natural language descriptions with visual or acoustic observations in a shared Hamming space. Our approach leverages pretrained wildlife foundation models, including BioCLIP and BioLingual, and adapts them efficiently for hashing using parameter-efficient fine-tuning. We evaluate our method on large-scale benchmarks, including iNaturalist2024 for text-to-image retrieval and iNatSounds2024 for text-to-audio retrieval, as well as multiple soundscape datasets to assess robustness under domain shift. Results show that retrieval using discrete hypercube embeddings achieves competitive, and in several cases superior, performance compared to continuous embeddings, while drastically reducing memory and search cost. Moreover, we observe that the hashing objective consistently improves the underlying encoder representations, leading to stronger retrieval and zero-shot generalization. These results demonstrate that binary, language-based retrieval enables scalable and efficient search over large wildlife archives for biodiversity monitoring systems.