Substantial Increase Research Articles

The Determinants of Early Investments in Urban School Systems in the United States

Abstract Growth in per pupil education spending in the United States was mostly flat until 1918, after which it increased by almost 100 percent in a brief six-year period. This is the fastest documented increase in per pupil education spending in U.S. history. Using newly digitized biennial data on 386 of the largest urban school systems in the United States from 1900 to 1930, I investigate the origins of this spending increase. I first document that there was significant expansion in all spending and revenue categories with particularly large increases in capital expenditures, which were likely financed through borrowing. My results suggest that state education policies were largely ineffective in increasing school resources, as laws increasing state aid to local districts crowded out local receipts while compulsory schooling and English-only laws were not accompanied by increases in receipts or expenditures per pupil. Rather, I find that substantial increases in educational spending per pupil were linked to women's suffrage. Providing women with the right to vote can explain about 20 percent of the increase in per pupil spending.

Improving efficiency through innovation in the ‘real-world’: Feasibility of a co-designed telehealth solution for individuals with aphasia

BACKGROUND: Research indicates that speech and language therapy for individuals with aphasia delivered via telehealth is acceptable and facilitates good outcomes. Although adoption of telehealth has increased following COVID-19, it has not been implemented broadly. Telehealth could assist services to meet the recommended intensity of therapy for individuals with aphasia. OBJECTIVE: The study aimed to investigate the barriers and facilitators to adopting telehealth for individuals with aphasia at two local NHS trusts; and to co-design a telehealth solution responding to these challenges. The feasibility of this solution was also investigated. A secondary objective is to highlight the value of real-world data (RWD) collection in evaluating clinical practice. METHODS: An experience-based co-design study was conducted, which developed and piloted a bespoke telehealth solution across service pathways at two NHS sites. Feasibility was evaluated qualitatively through interviews and quantitatively from RWD collected through the telehealth software. RESULTS: The telehealth solution incorporated outsourcing of the service to a specialist company, provision of hardware and software and regular support for individuals with aphasia and their carers. Take up was associated with a positive impact and the RWD revealed a substantial increase in the hours of therapy the individuals with aphasia received. CONCLUSION: Personalised telehealth solutions which respond to local and personal needs are feasible and an acceptable way to increase the intensity of speech and language therapy for some individuals with aphasia, bringing services more in line with evidence-based recommendations and optimising patient outcomes. Embedded RWD collection systems are valuable for evaluation.

On the impacts of higher tobacco taxes in Argentina: a computable general equilibrium approach

ObjectivesWe analyse the employment effects of increasing tobacco taxation in Argentina by building a computable general equilibrium (CGE) model.MethodsIn line with recent changes in tobacco taxation in the country, the...

COVID-19 and the Mental Health of Adolescents in British Columbia

Les auteurs ont relié les données individuelles sur les inscriptions scolaires, les services reçus de médecins et les médicaments sur ordonnance entre septembre 2016 et mars 2022 pour mesurer l'effet de la pandémie de COVID-19 et les perturbations s'y rapportant sur la fréquence mensuelle de traitements en santé mentale prodigués aux adolescents britanno-colombiens. Selon les résultats des augmentations importantes de traitement de la dépression et de l'anxiété, du trouble de déficit de l'attention/hyperactivité, des troubles des conduites alimentaires et d'autres troubles de santé mentale ont été observés chez les filles non autochtones dont la langue maternelle était l'anglais. Les filles dont la langue maternelle n’était pas l'anglais ont également reçu plus de traitements de la dépression et de l'anxiété, et les filles autochtones ont reçu plus de traitement du TDA/H. En revanche, les traitements pour les garçons n'ont pas changé et ont même diminué à l’égard de la plupart des troubles de santé mentale, sauf les troubles des conduites, qui ont augmenté chez les garçons dont la langue maternelle était l'anglais. Le traitement des troubles liés à une substance a diminué chez ces garçons, de même que chez les garçons et les filles autochtones. Les effets varient quelque peu en fonction de la situation socioéconomique, mais les auteurs n'ont observé aucune preuve de variation considérable selon le mode d'enseignement.

DATRA-MIV: Decoder-Adaptive Tiling and Rate Allocation for MPEG Immersive Video

The emerging immersive video coding standard moving picture experts group (MPEG) immersive video (MIV), which is ongoing standardization by MPEG-Immersive (MPEG-I) group, enables six degrees of freedom in a virtual reality environment that represents both natural and computer-generated scenes using multi-view video compression. The MIV eliminates the redundancy between multi-view videos and merges the residuals into multiple pictures, called an atlas. Thus, bitstreams with encoded atlases are generated and corresponding number of decoders are needed, which is challenging for the lightweight device with a single decoder. This article proposes a decoder-adaptive tiling and rate allocation method for MIV to overcome the challenge. First, the proposed method divides atlases into subpictures considering two aspects: (i) subpicture bitstream extracting and merging into one bitstream to use a single decoder and (ii) separation of each source view from the atlases for rate allocation. Second, the atlases are encoded by versatile video coding (VVC), using an extractable subpicture to divide the atlases into subpictures. Third, each subpicture bitstream is extracted, and asymmetric quality allocation for each subpictures is conducted by considering the residuals in the subpicture. Fourth, mixed-quality subpictures were merged by using the proposed bitstream merger. Fifth, the merged bitstream is decoded by using a single decoder. Finally, the viewing area of the user is synthesized by using the reconstructed atlases. Experimental results with the VVC test model (VTM) show that the proposed method achieves a 21.37% Bjøntegaard delta rate saving for immersive video peak signal-to-noise ratio and a 26.76% decoding runtime saving compared to the VTM anchor configuration. Moreover, it supports bitstreams for multiple decoders and single decoder without re-encoding, transcoding, or a substantial increase of the server-side storage.

Building Bridges and Widening Gaps

Abstract By exploiting the opening of the Öresund Bridge between Denmark and Sweden, we examine the impact of access to larger labor markets on wages and employment. We show that the bridge generated a substantial increase in the cross-country commuting behavior of Swedes and a 13.5% increase in the average wage of workers in the region. We further show that the wage gains are unevenly distributed: the effect is largest for high-educated men and smallest for low-educated women. Thus, the wage gains come at the cost of increased income inequality and a widening of the gender wage gap, both within and across households.

Efficient data management in Internet of Things: A survey of data aggregation techniques

The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed.

Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliver state-of-the-art performance across various NLP tasks. This has inspired researchers and practitioners to apply BERT to practical problems, such as information retrieval (IR). A survey that focuses on a comprehensive analysis of prevalent approaches that apply pretrained transformer encoders like BERT to IR can thus be useful for academia and the industry. In light of this, we revisit a variety of BERT-based methods in this survey, cover a wide range of techniques of IR, and group them into six high-level categories: (i) handling long documents, (ii) integrating semantic information, (iii) balancing effectiveness and efficiency, (iv) predicting the weights of terms, (v) query expansion, and (vi) document expansion. We also provide links to resources, including datasets and toolkits, for BERT-based IR systems. Additionally, we highlight the advantages of employing encoder-based BERT models in contrast to recent large language models like ChatGPT, which are decoder-based and demand extensive computational resources. Finally, we summarize the comprehensive outcomes of the survey and suggest directions for future research in the area.

Profitability Analysis of Markets Traded on Borsa Istanbul

Supermarkets significantly increased product prices in response to the sudden and sharp rise in the exchange rate in July 2018. A similar situation unfolded in 2021 when interest rates dropped, and the exchange rate experienced a steep increase. The surge in demand for foreign currency and subsequent interest rate cuts led to an escalation in the exchange rate. Consequently, production costs rose due to inflated raw material prices, resulting in higher product prices. Additionally, supermarkets allegedly engaged in cartel-like behavior, purposely withholding products from sale, further contributing to price hikes between 2021 and 2022. This study evaluates whether supermarkets garnered substantial profits during the period. Financial statement data from 2018 to 2022 was analyzed using vertical, trend, and ratio analyses to assess the financial position and profitability of five companies listed on Borsa Istanbul. Throughout the 2018-2022 period, except for 2022, BIM demonstrated the highest return on assets ratio. The return on assets ratios for 2022 and 2021 exhibited an upward trend, indicating an increase in profitability during 2022. SOK recorded the highest return on equity ratio in the 2018-2022 period, while CarrefourSA reported the lowest. Furthermore, the companies witnessed notable growth in revenues, gross sales profitability, and net profitability in 2022, surpassing previous years. This suggests they earned more profit by increasing prices at a greater rate than their costs in 2022. The operating profitability of the companies also experienced an upswing during this period. BIM held the largest share of operating profit in revenues in 2022, while CarrefourSA possessed the smallest share. In terms of net profitability, the companies fared better in 2022 compared to 2021, with net profitability exhibiting a more substantial increase during 2022.

Enhancing Structural Resilience: Exploring the Novel Sleeve Method for Steel T-Stub Connections

A new sleeve device has been introduced with the aim of enhancing the performance of steel structures under extreme loading conditions. Previous research has primarily focused on numerical simulations of this sleeve's application, emphasizing the need for further investigation to validate its effectiveness. This research paper presents a numerical analysis that explores how the sleeve device influences the deformation capacity and strength of a bolt up to the point of failure. The study involved the use of a T-stub connection with multiple sleeves, each featuring different geometric parameters. To assess the impact on strength and deformation capacity, a comparison was drawn between these findings and the behavior of a T-stub bolted connection without using the sleeve. In addition, Finite Element (FE) models were employed for additional parametric studies. The test outcomes demonstrated a substantial increase in the deformability of the bolted connection when utilizing the sleeve device, all the while preserving the strength and initial stiffness of the bolts. This proposed sleeve method in the study significantly enhances the connection's capacity by delaying bolt failure.

Evaluation of rock stability challenges affecting the formation of sustainable pit lake towards mine closure following crown pillar extraction in open pit-underground mines

The extraction of high-grade ore from the crown pillar (CP) in open pit-underground mines poses stability challenges and potential environmental risks. While an open pit has the potential to transition into a pit lake, the extraction of CP can induce failure in the surrounding walls, preventing the formation of the lake. There is also a concern that the backfilling material may not effectively confine toxic water within the pit, thereby risking contamination of the underground environment. To address these issues, a case study was conducted using FLAC3D and 3DEC models to evaluate the extent of failure caused by CP extraction. On-site observation, along with modelling, has revealed rock damage, including deformation stretching about 4 m from the pit wall and extending vertically from the pit floor to the ramp. The study identified three primary factors leading to pit wall failure or damage: steep pit slopes reaching approximately 70° near the pit floor, an underestimated CP thickness by about 4 m, and the concurrent extraction of ore from the pit wall alongside CP. Based on these findings, rehabilitation measures are suggested, including excavation of the deformed wall and cantilever, as well as partial pit backfilling. There is a substantial increase in the volume of backfill material as the extent of failure increases, which raise concerns about the decision-making process regarding CP extraction. Therefore, this article aims to raise environmental awareness and evaluate whether the benefits of ore extraction outweigh the considerations for pit wall support and the rehabilitation efforts during mine closure.

Optimization Of Hybrid Composite Laminates With Various Materials Using GA/GPSA Hybrid Algorithm for Maximum Dimensional Stability

In this study, it is aimed to obtain the highest dimensional stability against temperature changes in fiber-reinforced hybrid composite laminates considering eight different composite materials: Aramid/Epoxy, AS4/Epoxy, Boron/Epoxy, E-Glass/Epoxy, IM6/Epoxy, GY70/Epoxy, Kevlar49/Epoxy, and Spectra/Epoxy. The study focuses on finding the optimum continuous and traditional fiber angle orientations for the hybrid composite plates that would provide the lowest coefficients of thermal expansion. For this purpose, two different laminate sequences were investigated, each including two materials. A hybrid algorithm combining genetic algorithm and generalized pattern search algorithm was used in the optimization. A great number of hybrid design problems were solved repeatedly, and their results were compared both within themselves and to the optimum non-hybrid laminate results. Furthermore, the thermal durability of the selected optimum hybrid designs was evaluated by Tsai-Wu failure and Hashin-Rotem criteria. The results reveal that substantial increase in dimensional stability can be achieved by stacking sequence optimization of hybrid composite laminates with multiple material selection, and this hybrid design approach can offer the desired laminated composite structures for aerospace applications required to withstand extreme temperature changes.

Abstract 4936: An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations

Abstract The battle against many cancers and infectious diseases has long been hindered due to the complexity of finding potent and effective drug combinations. With each new drug considered, the number of combinations to potentially test increases exponentially, posing substantial challenges in screening throughput. These challenges further intensify when accounting for the number of doses of each drug that need to be tested in a drug combination matrix (known as matrix density). There is a pressing need to screen large amounts of combinations at sufficient density to discover new therapies for diseases like cancer, but this has traditionally been out of reach. However, the recent widespread adoption of acoustic liquid handling robots has shown promise to overcome these obstacles by allowing for intricate drug screening template designs which were previously not possible to make. Despite these advances, the throughput achieved by these technologies has been limited due to lack of broadly accessible protocols and analytical tools for drug combination screening. We present Combocat, an end-to-end platform that allows for substantial increases in throughput of drug combination screens by combining experimental protocols that can be deployed for acoustic liquid handlers, machine learning algorithms for data imputation, and software that allows for in-depth analysis of results. We first generated a reference dataset of over 250,000 unique drug combination measurements in multiple cancer cell lines. The combination data were collected in a dense format (10×10 combination matrices) using a novel drug-drug template and achieved a dramatic increase in throughput compared to conventional methods. We then used this dataset to build a computational model which allowed us to accurately estimate drug combination effects using sparse measurements and imputing non-measured values with machine learning. The sparse measurements are collected in 1536-well microplates and substantially boost the throughput capabilities of drug-drug screens. As proof-of-concept, we used our method to screen a preclinical model of neuroblastoma with 9,045 drug combinations. This represents 10% the scale of the largest drug combination studies ever reported, achieved using a fraction of the resources, and in dense formats. We validated our findings by re-screening top hits using the fully-measured, non-imputed method and demonstrate the accuracy of our platform. The Combocat platform’s documentation and codebase is open-source, and we also make a GUI available for interactive exploration of screening results. By integrating advanced experimental and computational methods, we provide a generalizable pipeline that will expedite synergy screens and the drug combination discovery process for many diseases. Citation Format: William C. Wright, Paul Geeleher. An ultrahigh-throughput synergy screening platform enables discovery of novel drug combinations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4936.

Granting leaders priority exit options promotes and jeopardizes cooperation in social dilemmas

Cooperation in human interactions often involves voluntary participation rather than obligatory requirements. This paper investigates the impact of voluntary exit strategies on cooperation within scale-free networks, employing spatial prisoner’s dilemma games as the framework. We analyze three distinct exit strategies: leader priority exit, fringe priority exit, and random exit, across varying dilemma strengths. Our findings reveal that in scenarios of weak dilemma strength, leader priority exit significantly diminishes cooperation, particularly when only a small subset of players is granted exit rights. In contrast, under strong dilemma strength conditions, both leader priority and random exits serve to unexpectedly bolster cooperation. This enhancement is primarily facilitated by high-degree nodes opting out, thereby enabling low-degree nodes to establish stable cooperation. Furthermore, our study underscores the pivotal role of network structure. In assortative networks, random exit emerges as the most effective strategy in promoting cooperation. On the other hand, disassortative networks exhibit a substantial increase in cooperation levels when employing leader priority exit. These results highlight the intricate interplay between exit strategies, dilemma strength, and network topology, offering critical insights for promoting cooperation in varied social contexts.

An adaptive threshold based gait authentication by incorporating quality measures

In this paper, an adaptive threshold-based gait authentication model is proposed, which incorporates the quality measure in the distance domain and maps them into the gradient domain to realize the optimal threshold of each gait sample, in contrast to the fixed threshold, as most of the authentication model utilizes. For accessing the quality measure of each gait, a gait covariate invariant generative adversarial network (GCI-GAN) is proposed to generate normal gait (canonical condition) irrespective of covariates (carrying, and viewing conditions) while preserving the subject identity. In particular, GCI-GAN connects to gradient weighted class activation mapping (Grad-CAMs) to obtain an attention mask from the significant components of input features, employs blending operation to manipulate specific regions of the input, and finally, multiple losses are employed to constrain the quality of generated samples. We validate the approach on gait datasets of CASIA-B and OU-ISIR and show a substantial increase in authentication rate over other state-of-the-art techniques.

Induction of SK-MEL-28 Invasion by Brain Cortical Cell-Conditioned Medium Through CXCL10 Signaling.

Melanoma, an infrequent yet significant variant of skin cancer, emerges as a primary cause of brain metastasis among various malignancies. Despite recognizing the involvement of inflammatory molecules, particularly chemokines, in shaping the metastatic microenvironment, the intricate cellular signaling mechanisms underlying cerebral metastasis remain elusive. In our pursuit to unravel the role of cytokines in melanoma metastasis, we devised a protocol utilizing mixed cerebral cortical cells and SK-MEL-28 melanoma cell lines. Contrary to expectations, we observed no discernible morphological change in melanoma cells exposed to a cerebral conditioned medium (CM). However, a substantial increase in both migration and proliferation was quantitatively noted. Profiling the chemokine secretion by melanoma in response to the cerebral CM unveiled the pivotal role of interferon gamma-induced protein 10 (CXCL10), inhibiting the secretion of interleukin 8 (CXCL8). Furthermore, through a transwell assay, we demonstrated that knockdown CXCL10 led to a significant decrease in the migration of the SK-MEL-28 cell line. In conclusion, our findings suggest that a cerebral CM induces melanoma cell migration, while modulating the secretion of CXCL10 and CXCL8 in the context of brain metastases. These insights advance our understanding of the underlying mechanisms in melanoma cerebral metastasis, paving the way for further exploration and targeted therapeutic interventions.

High‐performance tetraphenylethene‐containing poly (p‐phenylene terephthalamide) paper with aggregation‐induced emission characteristics

AbstractIn the rapidly advancing era of intelligent technologies, the development of advanced materials characterized by high performance and functionality is considered a crucial aspect. A series of high‐performance functional polymers known as cis/trans‐tetraphenylethene‐containing poly (p‐phenylene terephthalamide) (cis/trans‐TPE‐PPTA), has been developed. These polymers exhibited typical aggregation‐induced emission (AIE) characteristics along with exceptional electrical and mechanical properties. It has been observed that addition ratios of cis/trans‐TPE‐2NH2 as the comonomer significantly influenced the properties of the cis/trans‐TPE‐PPTA paper. Specifically, when compared with pure poly (p‐phenylene terephthalamide) (PPTA), the homogeneous trans‐TPE‐PPTA paper at 10% trans‐TPE‐2NH2 addition ratio, fabricated using a hot‐pressing method, demonstrated significantly improved overall performance, including a substantial increase in tensile strength (97.4%) and dielectric strength (41.5%), as well as other favorable properties. This enhancement suggests promising prospects for numerous applications.

Novel design and development of Centella Asiatica extract - loaded poloxamer/ZnO nanocomposite wound closure material to improve anti-bacterial action and enhanced wound healing efficacy in diabetic foot ulcer

Diabetic wounds can occur as a prevalent complication among people diagnosed with diabetes, frequently resulting in the necessity for amputation. The cause and effect of diabetic foot ulcer is complex, involving multiple factors. In the present study, wound healing strategies utilizing nanomaterials have proven to be effective in battling bacterial infections and improve wound regeneration. Poloxamers (PLX) exhibit extensive potential as a viable option for the development of nanomedicines owing to their inherent characteristics of self-assembly and encapsulation. This study aims to design and develop a PLX/ZnO nanocomposite incorporated with Centella Asiatica extract (CAE) for the multi-functional action in the diabetic wound healing treatment. Subsequently physico-chemical characterizations, such as XRD, FTIR, and TEM observations, demonstrated that the ZnO were evenly distributed through the PLX framework. The developed nanocomposite was biocompatible with mouse fibroblast cell line (L929), and it had multiple beneficial characteristics, such as a rapid self-healing process and effective antibacterial action against G+ and G− bacterial pathogens. After being treated with the developed formulation, skin fibroblast cell line and HUVECs demonstrated a substantial increase in their in vitro cell proliferation ability, migration, and tube-forming abilities. The utilization of a CAE@PLX/ZnO nanoformulation presents a viable strategy and a distinctive, encouraging composite for diabetic wound healing treatment.

Metabolites rapid-annotation in mice by comprehensive method of virtual polygons and Kendric mass loss filtering: A case study of Dendrobium nobile Lindl

With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.

Transforming Disposed Face Masks into S‐Doped Carbon Nanofibers for High Performance Supercapacitors

AbstractThe substantial increase in discarded face masks since the onset of the COVID‐19 pandemic has resulted in extensive environmental pollution from plastic waste. This study presents a novel upcycling method to repurpose disposable face masks into a carbon‐based nanomaterial for use in supercapacitors. The upcycled sample, CSDFM@800, demonstrates very good supercapacitor properties, boasting a high specific capacitance of 234 F g−1 at a current density of 0.5 A g−1. Additionally, the symmetric coin cell supercapacitor constructed from this material exhibits excellent performance, achieving a high energy density of 8.54 Wh kg−1 at a low current density of 0.5 A g−1, and maintaining superior capacitance retention of 96.8 % after 15,000 charge/discharge cycles at a current density of 10 A g−1. Materials characterisation results and electrochemical analysis reveal that this upcycling process not only preserves the carbon nanofiber structure but also introduces highly reactive sulfur as a heteroatom doping element.