Key Challenges Research Articles

Identification of Necessary Semantic Undertakers in the Causal View for Image-Text Matching

Image-text matching bridges vision and language, which is a fundamental task in multimodal intelligence. Its key challenge lies in how to capture visual-semantic relevance. Fine-grained semantic interactions come from fragment alignments between image regions and text words. However, not all fragments contribute to image-text relevance, and many existing methods are devoted to mining the vital ones to measure the relevance accurately. How well image and text relate depends on the degree of semantic sharing between them. Treating the degree as an effect and fragments as its possible causes, we define those indispensable causes for the generation of the degree as necessary undertakers, i.e., if any of them did not occur, the relevance would be no longer valid. In this paper, we revisit image-text matching in the causal view and uncover inherent causal properties of relevance generation. Then we propose a novel theoretical prototype for estimating the probability-of-necessity of fragments, PN_f, for the degree of semantic sharing by means of causal inference, and further design a Necessary Undertaker Identification Framework (NUIF) for image-text matching, which explicitly formalizes the fragment's contribution to image-text relevance by modeling PN_f in two ways. Extensive experiments show our method achieves state-of-the-art on benchmarks Flickr30K and MSCOCO.

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge: determining the reward model in the digital environment. This paper proposes a usability reward model in multi-agent reinforcement learning. Well-known mathematical formulas used for measuring usability metrics were analyzed in detail and incorporated into the usability reward model. In the usability reward model, any neural network-based multi-agent reinforcement learning algorithm can be used as the underlying learning algorithm. This paper presents a study using independent and actor-critic reinforcement learning algorithms to investigate their impact on the usability metrics of a mobile user interface. Computational experiments and usability tests were conducted in a specially designed multi-agent environment for mobile user interfaces, enabling the implementation of various usage scenarios and real-time adaptations.

Polyethylene Glycol-Based Polymer-Drug Conjugates: Novel Design and Synthesis Strategies for Enhanced Therapeutic Efficacy and Targeted Drug Delivery.

Due to their potential to enhance therapeutic results and enable targeted drug administration, polymer-drug conjugates that use polyethylene glycol (PEG) as both the polymer and the linker for drug conjugation have attracted much research. This study seeks to investigate recent developments in the design and synthesis of PEG-based polymer-drug conjugates, emphasizing fresh ideas that fill in existing knowledge gaps and satisfy the increasing need for more potent drug delivery methods. Through an extensive review of the existing literature, this study identifies key challenges and proposes innovative strategies for future investigations. The paper presents a comprehensive framework for designing and synthesizing PEG-based polymer-drug conjugates, including rational molecular design, linker selection, conjugation methods, and characterization techniques. To further emphasize the importance and adaptability of PEG-based polymer-drug conjugates, prospective applications are highlighted, including cancer treatment, infectious disorders, and chronic ailments.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

Standards for fecal microbiota transplant: Tools and therapeutic advances

Fecal microbiota transplantation (FMT) has been demonstrated to be efficacious in preventing recurrent Clostridioides difficile (C. difficile) infections, and is being investigated for treatment of several other diseases including inflammatory bowel disease, cancer, obesity, liver disease, and diabetes. To speed up the translation of FMT into clinical practice as a safe and standardized therapeutic intervention, additional evidence-based technical and regulatory guidance is needed. To this end in May of 2022, the International Alliance for Biological Standardization (IABS) and the BIOASTER Microbiology Technology Institute hosted a second webinar to discuss key issues still impeding the advancement and standardization of FMT. The goal of this two-day webinar was to provide a forum for scientific experts to share and discuss data and key challenges with one another. Discussion included a focus on the evaluation of safety, efficacy, clinical trial design, reproducibility and accuracy in obtained microbiome measurements and data reporting, and the potential for standardization across these areas. It also focused on increasing the application potential and visibility of FMT beyond treating C. difficile infections.

Non-steady-state stomatal conductance modeling and its implications: from leaf to ecosystem

Abstract. Accurate and efficient modeling of stomatal conductance (gs) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state gs and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis and often cannot reach a steady state before the next model time step, even on half-hourly timescales. Here, we implemented a simple dynamic gs model in the vegetation module of an LSM developed within the Climate Modeling Alliance and investigated the potential biases caused by the steady-state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of gs hysteresis response may not be substantial in terms of monthly integrated fluxes, our results highlight the importance of considering this effect when quantifying fluxes in the mornings and evenings, as well as interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Simulations also indicate that the biases in the integrated fluxes are more significant when stomata exhibit different speeds for opening and closure. Furthermore, prognostic modeling can bypass the A-Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study demonstrates the implications of dynamic gs modeling for improving the accuracy and efficiency of LSMs and for advancing our understanding of plant–environment interactions.

Strategic leadership for responsible artificial intelligence adoption in higher education

Artificial intelligence (AI) has the potential to transform higher education through its potential to improve learning, research, and leadership in colleges and universities. This study aims to identify key themes, challenges, and recommendations related to AI implementation in higher education and leadership. A literature review was supported by 2 LLMs. The results show that AI is being used to personalize teaching, provide formative feedback, identify at-risk students, accelerate research discovery, streamline administrative processes through chatbots, and optimize resource utilization. However, the findings also highlight significant technical, ethical, cultural and resource challenges that institutions must overcome in order to successfully harness AI's enormous potential while mitigating risks. Empowering management through knowledge, funding, and support structures that promote good governance and responsible AI adoption is fundamental to successfully harnessing the enormous but still largely untapped potential of AI.

Halide Superionic Conductors with Non‐Close‐Packed Anion Frameworks

AbstractHalide superionic conductors (SICs) are drawing significant research attention for their potential applications in all‐solid‐state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close‐packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close‐packed anion frameworks, we identify that the non‐close‐packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non‐close‐packed UCl3‐type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3‐type framework structure stems from its significantly more distorted site and larger diffusion channel than its close‐packed counterparts. By employing the non‐close‐packed anion framework as the key feature for high‐throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

Mononuclear Iron Pyridinethiolate Complex Promoted CO2 Photoreduction via Rapid Intramolecular Electron Transfer.

Designing earth-abundant metal complexes as efficient molecular photocatalysts for visible light-driven CO2 reduction is a key challenge in artificial photosynthesis. Here, we demonstrated the first example of a mononuclear iron pyridine-thiolate complex that functions both as a photosensitizer and catalyst for CO2 reduction. This single-component bifunctional molecular photocatalyst efficiently reduced CO2 to formate and CO with a total turnover number (TON) of 46 and turnover frequency (TOF) of 11.5 h-1 in 4 h under visible light irradiation. Notably, the quantum yield was determined to be 8.4 % for the generation of formate and CO at 400 nm. Quenching experiments indicate that high photocatalytic activity is mainly attributed to the rapid intramolecular quenching protocol. The mechanism investigation by DFT calculation and electrochemical studies revealed that the protonation of Febpy(pyS)2 is indispensable step for photocatalytic CO2 reduction.

Halide Superionic Conductors with Non-Close-Packed Anion Frameworks.

Halide superionic conductors (SICs) are drawing significant research attention for their potential applications in all-solid-state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close-packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close-packed anion frameworks, we identify that the non-close-packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non-close-packed UCl3-type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3-type framework structure stems from its significantly more distorted site and larger diffusion channel than its close-packed counterparts. By employing the non-close-packed anion framework as the key feature for high-throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

STDF: Spatio-Temporal Deformable Fusion for Video Quality Enhancement on Embedded Platforms

With the development of embedded systems and deep learning, it is feasible to combine them for offering various and convenient human-centered services, which is based on high-quality (HQ) videos. However, due to the limit of video traffic load and unavoidable noise, the visual quality of an image from an edge camera may degrade significantly, influencing the overall video and service quality. To maintain video stability, video quality enhancement (QE), aiming at recovering HQ videos from their distorted low-quality (LQ) sources, has aroused increasing attention in recent years. The key challenge for video QE lies in how to effectively aggregate complementary information from multiple frames (i.e., temporal fusion). To handle diverse motion in videos, existing methods commonly apply motion compensation before the temporal fusion. However, the motion field estimated from the distorted LQ video tends to be inaccurate and unreliable, thereby resulting in ineffective fusion and restoration. In addition, motion estimation for consecutive frames is generally conducted in a pairwise manner, which leads to expensive and inefficient computation. In this article, we propose a fast yet effective temporal fusion scheme for video QE by incorporating a novel Spatio-Temporal Deformable Convolution (STDC) to simultaneously compensate motion and aggregate temporal information. Specifically, the proposed temporal fusion scheme takes a target frame along with its adjacent reference frames as input to jointly estimate an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from multiple frames can be fused within the STDC operation in one forward pass. Extensive experimental results on three benchmark datasets show that our method performs favorably to the state of the art in terms of accuracy and efficiency.

ENHANCING MEDICATION QUALITY CONTROL IN NIGERIA: A COMPREHENSIVE ANALYSIS OF REGULATORY CHALLENGES AND SOLUTIONS

Medication quality control in Nigeria faces multifaceted challenges rooted in regulatory inefficiencies, infrastructural limitations, and systemic inadequacies. This study undertakes a comprehensive analysis of these challenges while proposing pragmatic solutions to bolster the quality control framework in the Nigerian pharmaceutical sector. Drawing upon extensive literature review and expert insights, the research identifies key regulatory challenges impeding effective medication quality control in Nigeria. These challenges encompass inadequate regulatory capacity, inconsistent enforcement mechanisms, rampant substandard and counterfeit medications, and limited access to essential medicines. Additionally, infrastructural deficiencies such as inadequate laboratory facilities and insufficient human resources further exacerbate the problem, leading to compromised patient safety and public health risks. In response to these challenges, the study proposes a multifaceted approach to enhance medication quality control in Nigeria. This includes strengthening regulatory frameworks through legislative reforms aimed at bolstering enforcement capabilities and harmonizing standards with international best practices. Moreover, improving regulatory capacity through training programs and investment in state-of-the-art laboratory infrastructure is essential to enhance surveillance and detection of substandard medications. Furthermore, fostering collaboration between regulatory agencies, healthcare providers, and pharmaceutical manufacturers is imperative to streamline supply chains and ensure the integrity of medications from production to distribution. Additionally, public awareness campaigns and community engagement initiatives play a pivotal role in empowering consumers to make informed decisions and report instances of substandard medications. By addressing these regulatory challenges and implementing comprehensive solutions, Nigeria can significantly enhance medication quality control, safeguard patient health, and foster a conducive environment for pharmaceutical innovation and growth.
 Keywords: Medical, Medication, Drugs, Quality Control, Nigeria, Regulations, Review.

A GIS-Based Top-Down Approach to Support Energy Retrofitting for Smart Urban Neighborhoods

Energy and environmental challenges are a major concern across the world and the urban residential building sector, being one of the main stakeholders in energy consumption and greenhouse gas emissions, needs to be more energy efficient and reduce carbon emissions. While it is easier to design net zero energy homes, existing home stocks are a major challenge for energy retrofitting. Two key challenges are determining the extent of retrofitting required, and developing knowledge-based effective policies that can be applied en-masse to housing stocks and neighborhoods. To overcome these challenges, it is essential to gather critical data about qualities of existing buildings including their age, geo-location, construction type, as well as electro-mechanical and occupancy parameters of each dwelling. The objective of this study was to develop a GIS-based model embedded with critical data of residential buildings to facilitate evidence-based retrofit programs for urban neighborhoods. A model based on a bottom-up approach was proposed in which information gathered from all stakeholders was inputted into one database that can be used for decision-making. A geo-located case study to validate a proposed GIS-based residential retrofitting model sample size of 74 residential buildings in the city of Riyadh was statistically analyzed and used. The results indicate behavior-based patterns, with a strong positive correlation (r = 0.606) between the number of occupants and number of household appliances, while regression analysis showed high occupancy rates do not necessarily result in high utility costs at the end of the month, and there is no statistical difference in the average monthly cost of gas between partial and fully occupied houses. Furthermore, neither the type of building, height, age, nor occupancy status play a significant role in the average energy consumed. Additionally, the GIS-based model was validated and found to be effective for energy-use mapping and gathering critical data for analyzing energy consumption patterns at neighborhood scale, making it useful for municipalities to develop effective policies aimed at energy efficient and smart neighborhoods, based on a recommended list of most effective energy-saving retrofit measures.

Urban Planning and Green Infrastructure Under a Changing Climate in Africa

This article uses the PRISMA systematic review method to identify the challenges and opportunities of urban planning in Africa, with a focus on climate change. The article emphasizes the importance of sustainable financing, transparent management, and effective governance. It also highlights financial difficulties, privatization, short political cycles, and institutional limitations as some of the key challenges that need to be addressed. Cultural, religious, and socio-economic influences on awareness of green infrastructure are examined, advocating for the promotion of environmental awareness and education. Climate risks threaten the sustainability of green infrastructure, necessitating resilient planning. Legal gaps hinder urban planning implementation, underscoring the need for appropriate legal frameworks. Coordination between government levels and long-term vision is essential to addressing political and administrative challenges. Overall, the article suggests sustainable, human-centered approaches and effective resource management for balanced urban development in Africa. It also emphasizes a holistic approach and ongoing collaboration for sustainability.

Application and challenges of artificial intelligence in cybersecurity

This paper delves into the multi-faceted role of Artificial Intelligence (AI) in the field of cybersecurity. It conducts an in-depth analysis of two common security problems, spam email and DDos/Dos attacks. It focuses on the application of AI in resolving these problems and further on Event Management (SIEM) and Intrusion Detection Systems (IDS). Furthermore, this article outlines and discusses five key challenges that AI may encounter throughout its development, including issues related to reliability, unidentified threats, data privacy, ethical considerations, and the importance of an explainable AI. The paper underscores the remarkable potential of AI to improve our defences against emerging cybersecurity threats. It highlights the growing importance of addressing ethical and legal issues in the evolving technological environment. Finally, the article offers a comprehensive summary and valuable insights into the future prospects of AI in the realm of cybersecurity, confirming its crucial role in shaping the security landscape in the digital era.

Quasi-Hyperbolic Framework Graphite Foam-Based Composites with High Thermal Conductivity and Electromagnetic Shielding Properties Fabricated by an Electrochemical Expansion Method.

Nowadays, the rapid development of electronic devices requires composites with high thermal conductivity and good electromagnetic shielding properties. The key challenge lies in the construction of high-performance conductive networks. Herein, an electrochemical expansion graphite foam (EEG) with a quasi-hyperbolic framework was prepared by an electrochemical expansion method, and then the epoxy resin (EP) was filled to fabricate the composites. The graphite plate was first electrochemically intercalated and then foamed, in which plasticization was caused by weak oxidation in intercalation and the quasi-hyperbolic framework was induced by foaming during expansion. These processes were characterized by Fourier transform infrared (FTIR), micro-Raman, X-ray photoelectron spectroscopy (XPS), and so on. Based on the highly efficient quasi-hyperbolic framework and high-quality graphite structure, the thermal conductivity of the composite reached 43.523 W/(m·K), and total electromagnetic interference (EMI) shielding (SET) reached 105 dB. The heat transfer behavior was simulated by finite element analysis (FEA) in detail. This method of preparing high thermal conductivity and electromagnetic shielding materials has a good application prospect.

Surface functional modification of Nb2CTx MXene for high performance capacitive deionization

MXene, show a promising electrode material for capacitive deionization (CDI) due to their elevated conductivity and hydrophilic surface characteristics. Despite optimal electrochemical reaction conditions, MXene still exhibits unsatisfactory capacity. Enhancing capacitance in Nb2CTx MXene poses a key challenge, and addressing this involves leveraging pseudocapacitance through increased active site concentration. This study reports a method to notably improve the capacitance by incorporating cation intercalation and surface modification. After alkali treatment and thermal annealing, the obtained 400-KOH-Nb2C as a cathode in capacitive deionization, exhibiting notable salt adsorption capacity of 104.2 mg g−1 at 1.6 V and an efficient removal rate of 1.73 mg g−1 min−1, cyclic stability maintained over 200 cycles. This optimized electrochemical and desalination performance is due to the large interlayer spaces of Nb2CTx and the surface with lowest termination of -F and –OH group, promoting the delicate surface of Nb2CTx MXene and accelerating ion transport. Furthermore, the derivation of KNbO3 nanowires on 400-KOH-Nb2C surface further expands the layer spacing of MXene and enhances the active sites, and also acts as a protective layer to improve the stability of Nb2C, this effectively addressing challenges associated with desalination. This study offers a novel solution for improving Nb2CTx MXene electrochemical properties in CDI.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28µA mm-1 cm-2 for glucose and 0.87µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

Socio-Economic Conditions and Challenges Faced by Pottery Artisan: A Case Study of Haryana and Delhi

India is rich in cultural diversity, where traditional crafts and artistic expressions have flourished for centuries. Artisans have played an essential role in shaping identity and character. The pottery evidence can be traced back to the Neolithic age with the participation of various traditional knowledge. Pottery artisans hold a significant place as their artistic creations signify the culture, tradition, and history of the country. The pottery industries provide various opportunities like employment generation, increasing exports, etc. The study attempted to investigate the present status of the socio-economic conditions of the pottery artisans in Delhi and Haryana. Another objective of the study was to analyse the range of challenges faced by pottery artisans in the study area. The study aimed to provide actionable recommendations that can contribute to the growth, preservation and empowerment of pottery artisans. Primary data were used in the study. Snowball stratified random sampling was used in the study. Various indexes were constructed to identify the key challenges faced. The study showed that the pottery industry was suffering from competition, followed by an irregular supply of raw materials and quality issues. Government and non-government organisations can solve the problem and help grow and develop this particular industry.

Life cycle thinking for the assessment of waste and circular economy policy: status and perspectives from the EU example

Life cycle thinking methods such as life cycle assessment (LCA) and costing (LCC) were originally developed to assess the performance of products and services (business-making decisions). However, they are increasingly deployed to support policy-making along the entire policy cycle, including via impact assessment (IA) of different policy options. These applications are associated with a number of challenges, mainly related to the dynamic and prospective nature of policy IA, typically forward-looking into 10–20 years ahead. This requires the application of prospective scenario analyses to develop the baseline scenario, reflecting the ‘do nothing’ into the future (business-as-usual), and the alternative scenarios, reflecting the consequences incurred by the implementation of the policy options under study. Such prospective and broadened boundary nature of policy IA challenges traditional LCA/LCC practices mostly based on retrospective, static scenarios and datasets. The present study provides an overview of recent IA studies supporting waste policy to show the state-of-the-art and the main challenges associated with the application of life cycle methods in IA, focusing on the most recent EU waste and circular economy policies. Moreover, since specific and transparent guidance on how to implement consistently these methods in policy IA is lacking, the study provides an initial guidance for application of life cycle thinking in IA drawing upon the knowledge obtained conducting waste policy IA studies. Key challenges in the field are still to be addressed, and might inspire further research to improve the application of life cycle thinking to policy assessment.