This paper presents a novel framework for prioritizing urban green space development in Tehran using diverse socio-economic, environmental, and sensitivity indices. The indices were derived from various sources including Google Earth Engine, air pollution measurements, municipal reports and the Weather Research & Forecasting (WRF) model. The WRF model was used to estimate the air temperature at a 1 km resolution due to insufficient meteorological stations, yielding RMSE and MAE values of 0.96°C and 0.92°C, respectively. After data preparation, several machine learning models were used for binary vegetation cover classification including XGBoost, LightGBM, Random Forest (RF) and Extra Trees. RF achieved the highest performance, exceeding 94% in Overall Accuracy, Recall, and F1-score. Then, the probability of areas lacking vegetation cover was assessed using socio-economic, environmental and sensitivity indices. This resulted in the RF generating an urban green space development prioritization map. Feature Importance Analysis revealed that the most significant indices were nightly land surface temperature (LST) and sensitive population. Finally, the framework performance was validated through microclimate simulation to assess the critical areas after and before the green space development by green roofs. The simulation demonstrated reducing air temperature by up to 0.67°C after utilizing the green roof technology in critical areas. As a result, this framework provides a valuable tool for urban planners to develop green spaces.

Welcome to the Fox News Politics newsletter, with the latest updates on the Trump administration, Capitol Hill and more Fox News politics content. The Justice Department (DOJ) is moving funds formerly granted to groups supporting transgender ideology and diversity, equity and inclusion (DEI) initiatives to law enforcement, Fox News Digital has confirmed. A Justice Department official told Fox News Digital that the DOJ, under Attorney General Pam Bondi's watch, will "not waste" funds on DEI. "The Department of Justice under Pam Bondi will not waste discretionary funds on DEI passion projects that do not make Americans safer," the official told Fox News Digital. "We will use our money to get criminals off the streets, seize drugs, and in some cases, fund programs that deliver a tangible impact for victims of crime."…READ

The government stripped the transparency amendment, which was backed by peers in the bill's reading in the House of Lords last week, out of the draft text by invoking financial privilege, meaning there is no budget available for new regulations, during a Commons debate on Wednesday afternoon. There were 297 MPs who voted in favour of removing the amendment, while 168 opposed. The data protection minister, Chris Bryant, told MPs that although he recognised that for many in the creative industries this "feels like an apocalyptic moment", he did not think the transparency amendment delivered the required solutions, and he argued that changes needed to be completed "in the round and not just piecemeal". Lady Kidron said: "The government failed to answer its own backbenchers who repeatedly asked'if not now then when?' and the minister replied with roundtable reviews and spurious problems about technical solutions. It is for government to set the laws and incentivise companies to obey it not run roundtables trying to work out technical solutions that they are not fit to provide. "It is astonishing that a Labour government would abandon the labour force of an entire sector.

Apple users--specifically those who use Siri through products such as Macbooks, iPhones, and Apple TVs--may be entitled to make a claim after Apple's class action lawsuit settlement, worth 95 million dollars, regarding the voice-activated assistant. The settlement comes from a lawsuit filed in 2021 by Californian Fumiko Lopez, who claimed that Apple, via Siri, conducted "unlawful and intentional interception and recording of individuals' confidential communications without their consent and subsequent unauthorized disclosure of those communications." "Apple intentionally, willfully, and knowingly violated consumers' privacy rights, including within the sanctity of consumers' own homes where they have the greatest expectation of privacy," the lawsuit stated. "Plaintiffs and Class Members would not have bought their Siri Devices, or would have paid less for them, if they had known Apple was intercepting, recording, disclosing, and otherwise misusing their conversations without consent or authorization." In 2019, Apple published a statement titled "Improving Siri's privacy protections," in which they said they hadn't "been fully living up" to their "high ideals" and vowed to issue improvements.

Three far-right extremists who amassed hundreds of weapons and planned to carry out attacks on targets including a mosque have been convicted of terrorism offences. Brogan Stewart, 25, from West Yorkshire, Christopher Ringrose, 34, from Staffordshire, and Marco Pitzettu, 25, from Derbyshire, were part of an online group who "idolised the Nazi regime". Sheffield Crown Court was told how Stewart had detailed torturing a Muslim leader using an "information extraction kit". All three were found guilty of terrorism offences at the same court on Wednesday and are due to be sentenced on 17 July.Counter Terrorism Policing North EastThe trio had amassed a cache of weapons as part of their planning During the nine-week trial, the court heard more than 200 weapons including machetes, hunting knives, swords and crossbows were found at their homes. Ringrose had also begun to build a 3D-printed semi-automatic firearm, which counter-terror police said would have been a "lethal weapon".

The CyberGuy Kurt Knutsson joins'Fox & Friends' to discuss the U.S.-Saudi investment summit and the debate over regulation as artificial intelligence continues to advance. Several nations met at the United Nations (U.N.) on Monday to revisit a topic that the international body has been discussing for over a decade: the lack of regulations on lethal autonomous weapons systems (LAWS), often referred to as "killer robots." This latest round of talks comes as wars rage in Ukraine and Gaza. While the meeting was held behind closed doors, U.N. Secretary-General António Guterres released a statement doubling down on his 2026 deadline for a legally binding solution to threats posed by LAWS. "Machines that have the power and discretion to take human lives without human control are politically unacceptable, morally repugnant and should be banned by international law," Guterres said in a statement.

Topic modeling in Italian legal research is hindered by the lack of public datasets, limiting the analysis of legal themes in Supreme Court judgments. To address this, we developed a document processing pipeline that produces an anonymized dataset optimized for topic modeling. The pipeline integrates document layout analysis (YOLOv8x), optical character recognition, and text anonymization. The DLA module achieved a mAP@50 of 0.964 and a mAP@50-95 of 0.800. The OCR detector reached a mAP@50-95 of 0.9022, and the text recognizer (TrOCR) obtained a character error rate of 0.0047 and a word error rate of 0.0248. Compared to OCR-only methods, our dataset improved topic modeling with a diversity score of 0.6198 and a coherence score of 0.6638. We applied BERTopic to extract topics and used large language models to generate labels and summaries. Outputs were evaluated against domain expert interpretations. Claude Sonnet 3.7 achieved a BERTScore F1 of 0.8119 for labeling and 0.9130 for summarization.

As generative AI becomes increasingly embedded in everyday workflows, it is important to evaluate its performance in ways that reflect real-world usage rather than abstract notions of intelligence. Unlike many existing benchmarks that assess general intelligence, our approach focuses on real-world utility, evaluating how well models support users in everyday tasks. While current benchmarks emphasize code generation or factual recall, users rely on AI for a much broader range of activities-from writing assistance and summarization to citation formatting and stylistic feedback. In this paper, we analyze large-scale survey data and usage logs to identify six core capabilities that represent how people commonly use Large Language Models (LLMs): Summarization, Technical Assistance, Reviewing Work, Data Structuring, Generation, and Information Retrieval. We then assess the extent to which existing benchmarks cover these capabilities, revealing significant gaps in coverage, efficiency measurement, and interpretability. Drawing on this analysis, we use human-centered criteria to identify gaps in how well current benchmarks reflect common usage that is grounded in five practical criteria: coherence, accuracy, clarity, relevance, and efficiency. For four of the six capabilities, we identify the benchmarks that best align with real-world tasks and use them to compare leading models. We find that Google Gemini outperforms other models-including OpenAI's GPT, xAI's Grok, Meta's LLaMA, Anthropic's Claude, DeepSeek, and Qwen from Alibaba-on these utility-focused metrics.

Censored response variables--where outcomes are only partially observed due to known bounds--arise in numerous scientific domains and present serious challenges for regression analysis. The Tobit model, a classical solution for handling left-censoring, has been widely used in economics and beyond. However, with the increasing prevalence of high-dimensional data, where the number of covariates exceeds the sample size, traditional Tobit methods become inadequate. While frequentist approaches for high-dimensional Tobit regression have recently been developed, notably through Lasso-based estimators, the Bayesian literature remains sparse and lacks theoretical guarantees. In this work, we propose a novel Bayesian framework for high-dimensional Tobit regression that addresses both censoring and sparsity. Our method leverages the Horseshoe prior to induce shrinkage and employs a data augmentation strategy to facilitate efficient posterior computation via Gibbs sampling. We establish posterior consistency and derive concentration rates under sparsity, providing the first theoretical results for Bayesian Tobit models in high dimensions. Numerical experiments show that our approach outperforms favorably with the recent Lasso-Tobit method. Our method is implemented in the R package tobitbayes, which can be found on Github.

Advanced Metering Infrastructure (AMI) data from smart electric and gas meters enables valuable insights for utilities and consumers, but also raises significant privacy concerns. In California, regulatory decisions (CPUC D.11-07-056 and D.11-08-045) mandate strict privacy protections for customer energy usage data, guided by the Fair Information Practice Principles (FIPPs). We comprehensively explore solutions drawn from data anonymization, privacy-preserving machine learning (differential privacy and federated learning), synthetic data generation, and cryptographic techniques (secure multiparty computation, homomorphic encryption). This allows advanced analytics, including machine learning models, statistical and econometric analysis on energy consumption data, to be performed without compromising individual privacy. We evaluate each technique's theoretical foundations, effectiveness, and trade-offs in the context of utility data analytics, and we propose an integrated architecture that combines these methods to meet real-world needs. The proposed hybrid architecture is designed to ensure compliance with California's privacy rules and FIPPs while enabling useful analytics, from forecasting and personalized insights to academic research and econometrics, while strictly protecting individual privacy. Mathematical definitions and derivations are provided where appropriate to demonstrate privacy guarantees and utility implications rigorously. We include comparative evaluations of the techniques, an architecture diagram, and flowcharts to illustrate how they work together in practice. The result is a blueprint for utility data scientists and engineers to implement privacy-by-design in AMI data handling, supporting both data-driven innovation and strict regulatory compliance.