West Midlands Police has admitted it overstated the evidence used to make the decision to ban Israeli fans from a match in Birmingham. Craig Guildford, its former chief constable, retired earlier this month after damning criticism of the ban on Maccabi Tel Aviv fans from the Europa League match against Aston Villa, last November. In newly released documents, the force also said we did not engage early enough with the local Jewish community, and indicated there was now a ban on AI use after its evidence included a match that did not take place. Furthermore, it said its operations would have lasted four days, involved multiple forces, and cost more than £5m, if 2,500 away fans had attended. The documents were released ahead of a public meeting on Tuesday, at which Police and Crime Commissioner for the West Midlands, Simon Foster, will discuss at his accountability and governance board, the decision to ban the Maccabi fans.

Home Secretary Shabana Mahmood says she has lost confidence in West Midlands Police's chief constable after Israeli football fans were banned from a match against Aston Villa. Mahmood told MPs a damning review from the policing watchdog over the intelligence that led to Maccabi Tel Aviv fans being banned showed a failure of leadership. The force has apologised saying it did not deliberately distort evidence that was used by Birmingham's Safety Advisory Group for the 6 November game . Chief Constable Craig Guildford remains in post, but faces a meeting on 27 January to be questioned by Police and Crime Commissioner Simon Foster who has the authority to sack him. Mahmood told the Commons on Wednesday she intended to restore the power for home secretaries to dismiss chief constables who fail their communities.

The government is set to announce its intention to build a new rail link between Birmingham and Manchester, the BBC understands. Previous plans for the HS2 high-speed rail line had included a line between the two cities, but that part of the project was scrapped by Rishi Sunak's government. On Wednesday, the government is expected to confirm proposals for new and improved rail links across the North of England in a scheme known as Northern Powerhouse Rail (NPR). Little detail about a new Birmingham to Manchester route is anticipated, other than the intention to build it after NPR is completed, meaning it may not happen for decades. HS2 is currently tens of billions of pounds over budget and around a decade behind schedule.

Warnings of disruption and protests have come from police as more than 700 officers prepare to mount an operation in Birmingham for Aston Villa's Uefa Europa League match against Maccabi Tel Aviv. Officers will be keeping the public safe and to tackle any crime and disorder on Thursday, West Midlands Police said, with police horses, dogs, the force's drone unit, and road policing officers out in the city. Planned protests include one by supporters of Palestine, who want the match to be called off. Last month, a decision to ban Tel Aviv fans from the event became the focus of parliamentary-level debate . The Israeli club later said supporters would not travel to Birmingham for safety reasons.

Unscrupulous foreign firms are using AI-generated images and false back stories to pose as family-run UK businesses to lure in shoppers. Customers say they feel completely ripped off after believing they were buying from independent boutiques in England but were delivered cheap clothes and jewellery, mass-shipped from warehouses in east Asia. Among the websites is C'est La Vie, a shop purporting to be run by couple Eileen and Patrick for 29 years and based in Birmingham's historic Jewellery Quarter - but with a returns address in China. Consumer guide Which? said the growing use of AI tools was making it possible for fraudsters to mislead the public on an unprecedented scale. Another website appearing to use AI-generated images is Mabel & Daisy, a seemingly quintessential, mother and daughter-owned clothing firm, which claims to be based in Bristol but has an address in Hong Kong.

Software that can see opponents through walls. Aimbots that can lock onto other players automatically. Tools that can boost characters' stats to the max. The world of online game cheats is expansive--with some cheat websites advertising hacks for dozens of PC games--and it's being driven by an underground economy that's allegedly raking in millions every year. Over the last two years, a group of computer scientists has been analyzing and mapping the online cheat marketplace, observing what behaviors get people banned from games, and probing the effectiveness of anti-cheat systems created by games developers.

This study presents a deep learning-based framework for predicting emergency department (ED) boarding counts six hours in advance using only operational and contextual data, without patient-level information. Data from ED tracking systems, inpatient census, weather, holidays, and local events were aggregated hourly and processed with comprehensive feature engineering. The mean ED boarding count was 28.7 (standard deviation = 11.2). Multiple deep learning models, including ResNetPlus, TSTPlus, and TSiTPlus, were trained and optimized using Optuna, with TSTPlus achieving the best results (mean absolute error = 4.30, mean squared error = 29.47, R2 = 0.79). The framework accurately forecasted boarding counts, including during extreme periods, and demonstrated that broader input features improve predictive accuracy. This approach supports proactive hospital management and offers a practical method for mitigating ED overcrowding.

Antibody-facilitated immune responses are central to the body's defense against pathogens, viruses, and other foreign invaders. The ability of antibodies to specifically bind and neutralize antigens is vital for maintaining immunity. Over the past few decades, bioengineering advancements have significantly accelerated therapeutic antibody development. These antibody-derived drugs have shown remarkable efficacy, particularly in treating cancer, SARS-CoV-2, autoimmune disorders, and infectious diseases. Traditionally, experimental methods for affinity measurement have been time-consuming and expensive. With the advent of artificial intelligence, in silico medicine has been revolutionized; recent developments in machine learning, particularly the use of large language models (LLMs) for representing antibodies, have opened up new avenues for AI-based design and improved affinity prediction. Herein, we present an advanced antibody-antigen binding affinity prediction model (LlamaAffinity), leveraging an open-source Llama 3 backbone and antibody sequence data sourced from the Observed Antibody Space (OAS) database. The proposed approach shows significant improvement over existing state-of-the-art (SOTA) methods (AntiFormer, AntiBERTa, AntiBERTy) across multiple evaluation metrics. Specifically, the model achieved an accuracy of 0.9640, an F1-score of 0.9643, a precision of 0.9702, a recall of 0.9586, and an AUC-ROC of 0.9936. Moreover, this strategy unveiled higher computational efficiency, with a five-fold average cumulative training time of only 0.46 hours, significantly lower than in previous studies.

In less than a year-and-a-half, Frankie Johnson, a man incarcerated at the William E Donaldson prison outside Birmingham, Alabama, says he was stabbed around 20 times. In December of 2019, Johnson says, he was stabbed "at least nine times" in his housing unit. In March of 2020, an officer handcuffed him to a desk following a group therapy meeting, and left the unit, after which another prisoner came in and stabbed him five times. In November of the same year, Johnson says, he was handcuffed by an officer and brought to the prison yard, where another prisoner attacked him with an ice pick, stabbing him "five to six times", as two correctional officers looked on. According to Johnson, one of the officers had actually encouraged his attacker to carry out the assault in retaliation for a previous argument between Johnson and the officer.

Background: Emergency department (ED) overcrowding remains a major challenge, causing delays in care and increased operational strain. Hospital management often reacts to congestion after it occurs. Machine learning predictive modeling offers a proactive approach by forecasting patient flow metrics, such as waiting count, to improve resource planning and hospital efficiency. Objective: This study develops machine learning models to predict ED waiting room occupancy at two time scales. The hourly model forecasts the waiting count six hours ahead (e.g., a 1 PM prediction for 7 PM), while the daily model estimates the average waiting count for the next 24 hours (e.g., a 5 PM prediction for the following day's average). These tools support staffing decisions and enable earlier interventions to reduce overcrowding. Methods: Data from a partner hospital's ED in the southeastern United States were used, integrating internal metrics and external features. Eleven machine learning algorithms, including traditional and deep learning models, were trained and evaluated. Feature combinations were optimized, and performance was assessed across varying patient volumes and hours. Results: TSiTPlus achieved the best hourly prediction (MAE: 4.19, MSE: 29.32). The mean hourly waiting count was 18.11, with a standard deviation of 9.77. Accuracy varied by hour, with MAEs ranging from 2.45 (11 PM) to 5.45 (8 PM). Extreme case analysis at one, two, and three standard deviations above the mean showed MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, XCMPlus performed best (MAE: 2.00, MSE: 6.64), with a daily mean of 18.11 and standard deviation of 4.51. Conclusions: These models accurately forecast ED waiting room occupancy and support proactive resource allocation. Their implementation has the potential to improve patient flow and reduce overcrowding in emergency care settings.