On-chip physiological mimicry of neurovascular unit: challenges and perspectives

Neurological disorders including neurodegenerative diseases,brain tumors,and stroke are the second leading cause of death and the greatest cause of disability worldwide.However,it remains challenging to achieve effective drug delivery to the central nervous system for treatments of neurological diseases due to the blood-brain barrier(BBB).The function of the BBB is regulated by the physiological interactions between various types of cells in the neurovascular unit(NVU).In the NVU,the brain vasculature of the BBB is surrounded by brain pericytes,brain astrocytes,neurons,and microglia(Figure 1).Moreover,the NVU at the levels of arteries and veins includes contractile smooth muscle cells(Schaeffer and Iadecola,2021).

Application of Optical Hydrogels in Environmental Sensing

The ever-increasing complexity of environmental pollutants urgently warrants the development of new detection technologies.Sensors based on the optical properties of hydrogels enabling fast and easy in situ detection are attracting increasing attention.In this paper,the data from 138 papers about different optical hydrogels(OHs)are extracted for statistical analysis.The detection performance and potential of various types of OHs in different environmental pollutant detection scenarios were evaluated and compared to those obtained using the standard detection method.Based on this analysis,the target recognition and sensing mechanisms of two main types of OHs are reviewed and discussed:photonic crystal hydrogels(PCHs)and fluorescent hydrogels(FHs).For PCHs,the environmental stimulus response,target receptors,inverse opal structures,and molecular imprinting techniques related to PCHs are reviewed and summarized.Furthermore,the different types of fluorophores(i.e.,compound probes,biomacromolecules,quantum dots,and luminescent microbes)of FHs are discussed.Finally,the potential academic research directions to address the challenges of applying and developing OHs in environmental sensing are proposed,including the fusion of various OHs,introduction of the latest technologies in various fields to the construction of OHs,and development of multifunctional sensor arrays.

Probing the Efficiency of PPMG-Based Composite Electrolytes for Applications of Proton Exchange Membrane Fuel Cell

PPMG-based composite electrolytes were fabricated via the solution method using the polyvinyl alcohol and polyvinylpyrrolidone blend reinforced with various contents of sulfonated inorganic filler.Sulfuric acid was employed as the sulfonating agent to functionalize the external surface of the inorganic filler,i.e.,graphene oxide.The proton conductivities of the newly prepared proton exchange membranes(PEMs)were increased by increasing the temperature and content of sulfonated graphene oxide(SGO),i.e.,ranging from 0.025 S/cm to 0.060 S/cm.The induction of the optimum level of SGO is determined to be an excellent route to enhance ionic conductivity.The single-cell performance test was conducted by sandwiching the newly prepared PEMs between an anode(0.2 mg/cm^(2) Pt/Ru)and a cathode(0.2 mg/cm^(2) Pt)to prepare membrane electrode assemblies,followed by hot pressing under a pressure of approximately 100 kg/cm^(2) at 60℃for 5–10 min.The highest power densities achieved with PPMG PEMs were 14.9 and 35.60 mW/cm^(2) at 25℃and 70℃,respectively,at ambient pressure with 100%relative humidity.Results showed that the newly prepared PEMs exhibit good electrochemical performance.The results indicated that the prepared composite membrane with 6 wt%filler can be used as an alternative membrane for applications of high-performance proton exchange membrane fuel cell.

Characteristic Study of Self-Powered Sensors Based on Native Protein Composite Film

Flexible electronic sensors composed of flexible film and conductive materials play an increasingly important role in wearable and internet information transmission.It has received more and more attention and made some progress over the decades.However,it is still a great challenge to prepare biocompatible and highly transparent conductive films.Egg white is a pure natural protein-rich material.Hydroxypropylmethyl cellulose has a good compatibility and high transparency,which is an ideal material for flexible sensors.Here,we overcome the problem of poor mechanical flexibility and electrical conductivity of protein,and develop a high transparency and good flexibility hydroxypropylmethyl cellulose/egg white protein composite membrane-based triboelectric nanogenerator('X'-TENG).The experimental results show that the flexible pressure sensor based on'X'-TENG has a high sensitivity,fast response speed,and low detection limit.It can even be used as a touch/pressure sensing artificial electronic skin.It can also be made into an intelligent waffle keyboard for recording and tracking users of the keyboard.Our strategy may provide a new way to easily build flexible electronic sensors and move toward practical applications.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Design & Test of an Advanced Web Security Analysis Tool (AWSAT)

Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Enhanced Memory-Safe Linux Security Modules (eLSMs) for Improving Security of Docker Containers for Data Centers

The adoption of Docker containers has revolutionized software deployment by providing a lightweight and efficient way to isolate applications in data centers. However, securing these containers, especially when handling sensitive data, poses significant challenges. Traditional Linux Security Modules (LSMs) such as SELinux and AppArmor have limitations in providing fine-grained access control to files within containers. This paper presents a novel approach using eBPF (extended Berkeley Packet Filter) to implement a LSM that focuses on file-oriented access control within Docker containers. The module allows the specification of policies that determine which programs can access sensitive files, providing enhanced security without relying solely on the host operating system’s major LSM.

Ad Blockers & Online Privacy: A Comparative Analysis of Privacy Enhancing Technologies (PET)

Online tracking mechanisms employed by internet companies for user profiling and targeted advertising raise major privacy concerns. Despite efforts to defend against these mechanisms, they continue to evolve, rendering many existing defences ineffective. This study performs a large-scale measurement of online tracking mechanisms across a large pool of websites using the OpenWPM (Open Web Privacy Measurement) platform. It systematically evaluates the effectiveness of several ad blockers and underlying Privacy Enhancing Technologies (PET) that are primarily used to mitigate different tracking techniques. By quantifying the strengths and limitations of these tools against modern tracking methods, the findings highlight gaps in existing privacy protections. Actionable recommendations are provided to enhance user privacy defences, guide tool developers and inform policymakers on addressing invasive online tracking practices.

Harvesting Energy Via Water Movement and Surface Ionics in Microfibrous Ceramic Wools

Due to the push for carbon neutrality in various human activities,the development of methods for producing electricity without relying on chemical reaction processes or heat sources has become highly significant.Also,the challenge lies in achieving microwatt-scale outputs due to the inherent conductivity of the materials and diverting electric currents.To address this challenge,our research has concentrated on utilizing nonconductive mediums for water-based low-cost microfibrous ceramic wools in conjunction with a NaCl aqueous solution for power generation.The main source of electricity originates from the directed movement of water molecules and surface ions through densely packed microfibrous ceramic wools due to the effect of dynamic electric double layer.This occurrence bears resemblance to the natural water transpiration in plants,thereby presenting a fresh and straightforward approach for producing electricity in an ecofriendly manner.The generator module demonstrated in this study,measuring 12×6 cm^(2),exhibited a noteworthy open-circuit voltage of 0.35 V,coupled with a short-circuit current of 0.51 mA.Such low-cost ceramic wools are suitable for ubiquitous,permanent energy sources and hold potential for use as self-powered sensors and systems,eliminating the requirement for external energy sources such as sunlight or heat.

Cybersecurity Risk Management through Behavior-Based Contextual Analysis of Online Logs

This paper studies cyber risk management by integrating contextual log analysis with User and Entity Behavior Analytics (UEBA). Leveraging Python scripting and PostgreSQL database management, the solution enriches log data with contextual and behavioral information from Linux system logs and semantic datasets. By incorporating Common Vulnerability Scoring System (CVSS) metrics and customized risk scoring algorithms, the system calculates Insider Threat scores to identify potential security breaches. The integration of contextual log analysis and UEBA [1] offers a proactive defense against insider threats, reducing false positives and prioritizing high-risk alerts.

A Comparative Analysis of Cybersecurity Threat Taxonomies for Healthcare Organizations

Information technology is critical in coordinating patient records, smart devices, operations, and critical infrastructure in healthcare organizations, and their constantly changing digital environment, including suppliers, doctors, insurance providers, and regulatory agencies. This dependence on interdependent systems makes this sector vulnerable to various information technology risks. Such threats include common cybersecurity risks such as data breaches and malware attacks, unique problems occurring in healthcare settings such as unauthorized access to patient records, disruptions in services provided at medical facilities, and potential harm caused to patients due to the compromise of medical devices. The threat taxonomies, such as the Open Threat Taxonomy, NIST, or ENISA, are foundational frameworks for grasping and categorizing IT threats. However, these taxonomies were not specifically designed to deal with the complexities of the healthcare industry. The problem arises from the gap between these taxonomies’ general nature and the industry-specific threats and vulnerabilities that affect healthcare organizations. As a result, many healthcare institutions fail to holistically address and eliminate the unique risks related to confidentiality, integrity, and availability of patients’ data as well as critical systems used in healthcare. This paper aims to narrow this gap by carefully assessing these taxonomies to determine the frame-work best suited for addressing the threat environment in the healthcare sector.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Convergence Issues Associated with Cutoff Energies and Ab Initio Studies of Adsorption of CO on W and Pt

The experimental research programs of 1950s, to understand the adsorption of CO on W surfaces, changed to ab initio studies in 2000s. The goals were to seek improved practical applications. Most of the studies were based on density functional theory. Many studies also used programs, such as VASP (Vienna Abinitio simulation package) and CPMD. The computational procedures used plane wave approximations. This needed studies with selection of K points and cutoff energy selection to assure convergence in energy calculations. Observations and analysis of papers published from 2006 to 2022 indicate that the cutoff energies were selected arbitrarily without any needed convergence studies. By selecting a published 2006 paper, this paper has clearly showed that an arbitrary selection of cutoff energy, such as 460 eV, is not in the range of, cutoff energies that assure convergence of energy calculations, with ab initio methods and have indicated correction procedures. .

Insight into the Disciplinary Structure of Nanoscience & Nanotechnology

Purpose： This paper aims to gain an insight into the disciplinary structure of nanoscience ＆ nanotechnology （N＆N）： What is the disciplinary network of N＆N like？ Which disciplines are being integrated into N＆N over time？ For a specific discipline, how many other disciplines have direct or indirect connections with it？ What are the distinct subgroups of N＆N at different evolutionary stages？ Such critical issues are to be addressed in this paper. Design/methodology/approach： We map the disciplinary network structure of N＆N by employing the social network analysis tool, Netdraw, identifying which Web of Science Categories （WCs） mediate nbetweenness centrality in different stages of nano development. Cliques analysis embedded in the Ucinet program is applied to do the disciplinary cluster analysis in the study according to the path of ＂Network-Subgroup-Cliques,＂ and a tree diagram is selected as the visualizing type. Findings： The disciplinary network structure reveals the relationships among different disciplines in the N＆N developing process clearly, and it is easy for us to identify which disciplines are connected with the core ＂N＆N＂ directly or indirectly. The tree diagram showing N＆N related disciplines provides an interesting perspective on nano research and development （R＆D） structure. Research limitations： The matrices used to draw the N＆N disciplinary network are the original ones, and normalized matrix could be tried in future similar studies. Practical implications： Results in this paper can help us better understand the disciplinary structure of N＆N, and the dynamic evolution of N＆N related disciplines over time. The findings could benefit R＆D decision making. It can support policy makers from government agencies engaging in science and technology （S＆T） management or S＆T strategy planners to formulate efficient decisions according to a perspective of converging sciences and technologies. Originality/value： The novelty of this study lies in mapping the disciplinary network structure of N＆N clearly, identifying which WCs have a mediating effect in different developmental stages （especially analyzing clusters among disciplines related to N＆N, revealing close or distant relationships among distinct areas pertinent to N＆N）.

Regulating the Electrical and Mechanical Properties of TaS_(2) Films via van der Waals and Electrostatic Interaction for High Performance Electromagnetic Interference Shielding

Low-dimensional transition metal dichalcogenides(TMDs) have unique electronic structure, vibration modes, and physicochemical properties, making them suitable for fundamental studies and cutting-edge applications such as silicon electronics, optoelectronics, and bioelectronics. However, the brittleness, low toughness,and poor mechanical and electrical stabilities of TMD-based films limit their application. Herein, a TaS_(2) freestanding film with ultralow void ratio of 6.01% is restacked under the effect of bond-free van der Waals(vdW) interactions within the staggered 2H-TaS_(2) nanosheets.The restacked films demonstrated an exceptionally high electrical conductivity of 2,666 S cm^(-1), electromagnetic interference shielding effectiveness(EMI SE) of 41.8 dB, and absolute EMI SE(SSE/t) of 27,859 dB cm^(2) g^(-1), which is the highest value reported for TMD-based materials. The bond-free vdW interactions between the adjacent 2H-TaS_(2) nanosheets provide a natural interfacial strain relaxation, achieving excellent flexibility without rupture after 1,000 bends. In addition, the TaS_(2) nanosheets are further combined with the polymer fibers of bacterial cellulose and aramid nanofibers via electrostatic interactions to significantly enhance the tensile strength and flexibility of the films while maintaining their high electrical conductivity and EMI SE.This work provides promising alternatives for conventional materials used in EMI shielding and nanodevices.

Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things

In the era of 5G and the Internet of things(IoTs),vari-ous human-computer interaction systems based on the integration of triboelectric nanogenerators(TENGs)and IoTs technologies dem-onstrate the feasibility of sustainable and self-powered functional systems.The rapid development of intelligent applications of IoTs based on TENGs mainly relies on supplying the harvested mechanical energy from surroundings and implementing active sensing,which have greatly changed the way of human production and daily life.This review mainly introduced the TENG applications in multidisci-pline scenarios of IoTs,including smart agriculture,smart industry,smart city,emergency monitoring,and machine learning-assisted artificial intelligence applications.The challenges and future research directions of TENG toward IoTs have also been proposed.The exten-sive developments and applications of TENG will push forward the IoTs into an energy autonomy fashion.

Impact of Low-Impact Development Technologies from an Ecological Perspective in Different Residential Zones of the City of Atlanta, Georgia

Low-impact development （LID） technologies have a great potential to reduce water usage and stormwa- ter runoff and are therefore seen as sustainable improvements that can be made to traditional water infrastructure, These technologies include bioretention areas, rainwater capturing, and xeriscaping, all of which can be used in residential zones, Within the City of Atlanta, residential water usage accounts for 53% of the total water consumption; therefore, residential zones offer significant impact potential for the implementation of LID, This study analyzes the use of LID strategies within the different residen- tial zones of the City of Atlanta from an ecological perspective by drawing analogies to natural ecosys- tems, The analysis shows that these technologies, especially with the addition of a graywater system, work to improve the conventional residential water network based upon these ecological metrics, The higher metric values suggest greater parity with healthy, natural ecosystems.

A class of Ga-Al-P-based compounds with disordered lattice as advanced anode materials for Li-ion batteries

Phosphides possess large reversible capacity, small voltage hysteresis, and high energy efficiency, thus promising to be new anode candidates to replace commercial graphite for Li-ion batteries(LIBs).Through a facile mechanochemistry method, we prepare a novel ternary phosphide of Ga0.5Al0.5P whose crystalline structure is determined to be a cation-disordered cubic zinc sulfide structure according to XRD refinement. As an anode for LIBs, the Ga0.5Al0.5P delivers a reversible capacity of 1,352 mA h g^(-1)at100 mA g^(-1)with an initial Coulombic efficiency(ICE) up to 90.0% based on a reversible Li-storage mechanism integrating intercalation and subsequent conversion processes as confirmed by various characterizations techniques including in-situ XRD, ex-situ Raman, and XPS and electrochemical characterizations.Graphite-modified Ga0.5Al0.5P exhibits a long-lasting cycling stability of retaining 1,182 mA h g^(-1)after300 cycles at 100 m A g^(-1), and 625 mA h g^(-1)after 800 cycles at 2,000 mA g^(-1), and a high-rate performance of remaining 342 m A h g^(-1)at 20,000 mA g^(-1). The outstanding electrochemical performances can be attributed to enhanced reaction kinetics enabled by the capacitive behaviors and the faster Liion diffusion enabled by the cation-mixing. Importantly, by tuning the cationic ratio, we develop a novel series of cation-mixed compounds of Ga_(1/3)Al_(2/3)P, Ga_(1/4)Al_(3/4)P, Ga_(1/5)Al_(4/5)P, Ga_(2/3)Al_(1/3)P, Ga_(3/4)Al_(1/4)P, and Ga_(4/5)Al_(1/5)P, which demonstrate large capacity, high ICE, and suitable anode potentials. Broadly, these compounds with disordered lattices probably present novel physicochemical properties, and high electrochemical performances, thus providing a new perspective for new materials design.

A 15-year-Long catalog of seismicity in the Eastern Tennessee Seismic Zone(ETSZ)using matched filter detection

We present a detailed catalog of 13671 earthquakes in the Eastern Tennessee Seismic Zone(ETSZ)that spans January 1,2005 to July 31,2020.We apply a matched filter detection technique on over 15 years of continuous data,resulting in arguably the most complete catalog of seismicity in the ETSZ yet.The magnitudes of newly detected events are determined by computing the amplitude ratio between the detections and templates using a principal component fit.We also compute the b-value for the new catalog and comparatively relocate a subset of newly detected events using XCORLOC and hypoDD,which shows a more defined structure at depth.We find the greatest concentration along and to the east of the New York-Alabama Lineament,as defined by the magnetic anomaly,supporting the argument that this feature likely is related to the generation of seismicity in the ETSZ.We examine seismicity in the vicinity of the Watts Bar Reservoir,which is located about 5 km from the epicenter of the M_(W) 4.4 December 12,2018 Decatur,Tennessee earthquake,and find possible evidence for reservoir modulated seismicity in this region.We also examine seismicity in the entire ETSZ to search for a correlation between shallow earthquakes and seasonal hydrologic changes.Our results show limited evidence for hydrologicallydriven shallow seismicity due to seasonal groundwater levels in the ETSZ,which contradicts previous studies hypothesizing that most intraplate earthquakes are associated with the dynamics of hydrologic cycles.